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\n  \n 2024\n \n \n (6)\n \n \n
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\n \n\n \n \n \n \n \n \n A revisited branch-and-cut algorithm for large-scale orienteering problems.\n \n \n \n \n\n\n \n Kobeaga, G.; Rojas-Delgado, J.; Merino, M.; and Lozano, J., A.\n\n\n \n\n\n\n European Journal of Operational Research, 313(1): 44-68. 2024.\n \n\n\n\n
\n\n\n\n \n \n \"AWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {A revisited branch-and-cut algorithm for large-scale orienteering problems},\n type = {article},\n year = {2024},\n keywords = {Branch-and-cut,Large problems,Orienteering problem,Routing},\n pages = {44-68},\n volume = {313},\n websites = {https://www.sciencedirect.com/science/article/pii/S0377221723005933},\n id = {098e1266-6d26-38f2-aa80-cba2fe8a030f},\n created = {2024-01-09T10:55:02.961Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2024-01-09T10:55:02.961Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {The orienteering problem is a route optimization problem which consists of finding a simple cycle that maximizes the total collected profit subject to a maximum distance limitation. In the last few decades, the occurrence of this problem in real-life applications has boosted the development of many heuristic algorithms to solve it. However, during the same period, not much research has been devoted to the field of exact algorithms for the orienteering problem. The aim of this work is to develop an exact method which is able to obtain the optimum in a wider set of instances than with previous methods, or to improve the lower and upper bounds in its disability. We propose a revisited version of the branch-and-cut algorithm for the orienteering problem which includes new contributions in the separation algorithms of inequalities stemming from the cycle problem, in the separation loop, in the variables pricing, and in the calculation of the lower and upper bounds of the problem. Our proposal is compared to three state-of-the-art algorithms on 258 benchmark instances with up to 7397 nodes. The computational experiments show the relevance of the designed components where 18 new optima, 76 new best-known solutions and 85 new upper-bound values were obtained.},\n bibtype = {article},\n author = {Kobeaga, Gorka and Rojas-Delgado, Jairo and Merino, María and Lozano, Jose A},\n doi = {https://doi.org/10.1016/j.ejor.2023.07.034},\n journal = {European Journal of Operational Research},\n number = {1}\n}
\n
\n\n\n
\n The orienteering problem is a route optimization problem which consists of finding a simple cycle that maximizes the total collected profit subject to a maximum distance limitation. In the last few decades, the occurrence of this problem in real-life applications has boosted the development of many heuristic algorithms to solve it. However, during the same period, not much research has been devoted to the field of exact algorithms for the orienteering problem. The aim of this work is to develop an exact method which is able to obtain the optimum in a wider set of instances than with previous methods, or to improve the lower and upper bounds in its disability. We propose a revisited version of the branch-and-cut algorithm for the orienteering problem which includes new contributions in the separation algorithms of inequalities stemming from the cycle problem, in the separation loop, in the variables pricing, and in the calculation of the lower and upper bounds of the problem. Our proposal is compared to three state-of-the-art algorithms on 258 benchmark instances with up to 7397 nodes. The computational experiments show the relevance of the designed components where 18 new optima, 76 new best-known solutions and 85 new upper-bound values were obtained.\n
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\n \n\n \n \n \n \n \n \n A probabilistic generative model to discover the treatments of coexisting diseases with missing data.\n \n \n \n \n\n\n \n Zaballa, O.; Pérez, A.; Gómez-Inhiesto, E.; Acaiturri-Ayesta, T.; and Lozano, J., A.\n\n\n \n\n\n\n Computer Methods and Programs in Biomedicine, 243: 107870. 2024.\n \n\n\n\n
\n\n\n\n \n \n \"AWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {A probabilistic generative model to discover the treatments of coexisting diseases with missing data},\n type = {article},\n year = {2024},\n keywords = {Comorbidity modeling,Electronic health records,Latent variable model,Markov model,Probabilistic generative model},\n pages = {107870},\n volume = {243},\n websites = {https://www.sciencedirect.com/science/article/pii/S0169260723005369},\n id = {10c3e4df-c95d-3165-bc9c-70628b36ddcb},\n created = {2024-01-09T10:56:44.804Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2024-01-09T10:56:44.804Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {Background and Objective\nComorbidities, defined as the presence of co-existing diseases, progress through complex temporal patterns among patients. Learning such dynamics from electronic health records is crucial for understanding the coevolution of diseases. In general, medical records are represented through temporal sequences of clinical variables together with their diagnosis. However, we consider the specific problem where most of the diagnoses are missing. We present a novel probabilistic generative model with a three-fold objective: (i) identify and segment the medical history of patients into treatments associated with comorbidities; (ii) learn the model associated with each identified disease treatment; and (iii) discover subtypes of patients with similar coevolution of comorbidities.\nMethods\nTo this end, the model considers a latent structure for the sequences, where patients are modeled by a latent class defined by the evolution of their comorbidities, and each observed medical event of their clinical history is associated with a latent disease. The learning process is performed using an Expectation-Maximization algorithm that considers the exponential number of configurations of the latent variables and is efficiently solved with dynamic programming.\nResults\nThe evaluation of the method is carried out both on synthetic and real world data: the experiments on synthetic data show that the learning procedure allows the generative model underlying the data to be recovered; the experiments on real medical data show accurate results in the segmentation of sequences into different treatments, subtyping of patients and diagnosis imputation.\nConclusion\nWe present an interpretable generative model that handles the incompleteness of EHRs and describes the different joint evolution of coexisting diseases depending on the active comorbidities of the patient at each moment.},\n bibtype = {article},\n author = {Zaballa, Onintze and Pérez, Aritz and Gómez-Inhiesto, Elisa and Acaiturri-Ayesta, Teresa and Lozano, Jose A},\n doi = {https://doi.org/10.1016/j.cmpb.2023.107870},\n journal = {Computer Methods and Programs in Biomedicine}\n}
\n
\n\n\n
\n Background and Objective\nComorbidities, defined as the presence of co-existing diseases, progress through complex temporal patterns among patients. Learning such dynamics from electronic health records is crucial for understanding the coevolution of diseases. In general, medical records are represented through temporal sequences of clinical variables together with their diagnosis. However, we consider the specific problem where most of the diagnoses are missing. We present a novel probabilistic generative model with a three-fold objective: (i) identify and segment the medical history of patients into treatments associated with comorbidities; (ii) learn the model associated with each identified disease treatment; and (iii) discover subtypes of patients with similar coevolution of comorbidities.\nMethods\nTo this end, the model considers a latent structure for the sequences, where patients are modeled by a latent class defined by the evolution of their comorbidities, and each observed medical event of their clinical history is associated with a latent disease. The learning process is performed using an Expectation-Maximization algorithm that considers the exponential number of configurations of the latent variables and is efficiently solved with dynamic programming.\nResults\nThe evaluation of the method is carried out both on synthetic and real world data: the experiments on synthetic data show that the learning procedure allows the generative model underlying the data to be recovered; the experiments on real medical data show accurate results in the segmentation of sequences into different treatments, subtyping of patients and diagnosis imputation.\nConclusion\nWe present an interpretable generative model that handles the incompleteness of EHRs and describes the different joint evolution of coexisting diseases depending on the active comorbidities of the patient at each moment.\n
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\n \n\n \n \n \n \n \n \n Filter method-based feature selection process for unattributed-identity multi-target regression problem.\n \n \n \n \n\n\n \n Garcia, I.; and Santana, R.\n\n\n \n\n\n\n Expert Systems with Applications, 246: 123245. 2024.\n \n\n\n\n
\n\n\n\n \n \n \"FilterWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Filter method-based feature selection process for unattributed-identity multi-target regression problem},\n type = {article},\n year = {2024},\n keywords = {Feature selection,Multi-target regression,Prediction,Stability},\n pages = {123245},\n volume = {246},\n websites = {https://www.sciencedirect.com/science/article/pii/S0957417424001106},\n id = {59a5b945-a46b-3269-927b-6ba54a3bebbf},\n created = {2024-01-22T09:19:32.474Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2024-01-22T09:19:32.474Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {Unattributed-identity multi-target regression (UIMTR) is defined as a multi-target regression problem in which the identity of the target and predictor variables is not predefined. It is a problem that can be found in several real-world applications. For example, when historical data is available from a set of devices, but real-time data can only be requested from a subset of them (so called sentinels). For estimating real-time status of non-sentinels, it will be necessary to generate multi-target regression models. Therefore, attributing the identity of the real-time communicators (sentinels), i.e., the predictor variables, is a critical aspect. Moreover, unlike classical feature selection problems, the set of target variables is determined after applying the selection methods and not before, thus, some adaptations are necessary. We introduce three novel methods to solve the UIMTR and, after extensive evaluation, we demonstrate: (i) the feasibility of the methods, (ii) the usefulness of the approach, and (iii) the improvement over other classical techniques. The results have been evaluated from three perspectives: (i) the quality of the predictions, (ii) the stability of the methods and (iii) the execution time.},\n bibtype = {article},\n author = {Garcia, Iker and Santana, Roberto},\n doi = {https://doi.org/10.1016/j.eswa.2024.123245},\n journal = {Expert Systems with Applications}\n}
\n
\n\n\n
\n Unattributed-identity multi-target regression (UIMTR) is defined as a multi-target regression problem in which the identity of the target and predictor variables is not predefined. It is a problem that can be found in several real-world applications. For example, when historical data is available from a set of devices, but real-time data can only be requested from a subset of them (so called sentinels). For estimating real-time status of non-sentinels, it will be necessary to generate multi-target regression models. Therefore, attributing the identity of the real-time communicators (sentinels), i.e., the predictor variables, is a critical aspect. Moreover, unlike classical feature selection problems, the set of target variables is determined after applying the selection methods and not before, thus, some adaptations are necessary. We introduce three novel methods to solve the UIMTR and, after extensive evaluation, we demonstrate: (i) the feasibility of the methods, (ii) the usefulness of the approach, and (iii) the improvement over other classical techniques. The results have been evaluated from three perspectives: (i) the quality of the predictions, (ii) the stability of the methods and (iii) the execution time.\n
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\n \n\n \n \n \n \n \n Understanding the Impact of Arbitration in MZI-Based Beneš Switching Fabrics.\n \n \n \n\n\n \n Navaridas, J.; Kynigos, M.; Pascual, J., A.; Lujan, M.; Miguel-Alonso, J.; and Goodacre, J.\n\n\n \n\n\n\n IEEE Transactions on Parallel & Distributed Systems, 35(02): 338-348. 2 2024.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Understanding the Impact of Arbitration in MZI-Based Beneš Switching Fabrics},\n type = {article},\n year = {2024},\n keywords = {photonics;optical switches;fabrics;routing;time division multiplexing;energy efficiency;wavelength division multiplexing},\n pages = {338-348},\n volume = {35},\n month = {2},\n publisher = {IEEE Computer Society},\n city = {Los Alamitos, CA, USA},\n id = {3c35af05-d96c-3709-bb37-30561b825bc0},\n created = {2024-01-26T11:11:09.842Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2024-01-26T11:11:09.842Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {ARTICLE},\n private_publication = {false},\n abstract = {Top-of-rack switches based on photonic switching fabrics (PSF) could provide higher bandwidth and energy efficiency for datacenters (DC) and high-performance computers (HPC) than these with traditional electronic crossbars. However, because of their bufferless nature, PFS are affected by contention much more drastically than traditional packet-switched electronic networks where traffic can advance towards its destination, getting buffered upon encountering contention and resuming transmission once resources are freed. In contrast, PSFs stop the injection of all traffic that generate contention. Consequently, it is important to understand how the order in which flows are serviced affects performance metrics. Our contribution is to quantify this impact through a comprehensive simulation-based evaluation focusing on a recently fabricated PSF prototype. Our experiments include configurations with three routing algorithms, two switching methods, three ToR switch sizes and 9 representative workloads from the DC and HPC domains. We found that the effect of arbitration on raw throughput is negligible but, when considering more realistic loads, selecting an appropriate arbitration policy can improve communication time and energy efficiency. Indeed, the communication time can be reduced by between 10% and 30% by employing appropriate arbitration. Switching energy efficiency can also be improved between 4% and 13%. Finally, insertion loss is barely affected, with differences below 2%. LFU and ARR were found to obtain the best results. LFU is very good with regular workloads but one of the worse with irregular workloads. ARR obtains good results regardless of the type of workload.},\n bibtype = {article},\n author = {Navaridas, J and Kynigos, M and Pascual, J A and Lujan, M and Miguel-Alonso, J and Goodacre, J},\n doi = {10.1109/TPDS.2023.3336703},\n journal = {IEEE Transactions on Parallel &amp; Distributed Systems},\n number = {02}\n}
\n
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\n Top-of-rack switches based on photonic switching fabrics (PSF) could provide higher bandwidth and energy efficiency for datacenters (DC) and high-performance computers (HPC) than these with traditional electronic crossbars. However, because of their bufferless nature, PFS are affected by contention much more drastically than traditional packet-switched electronic networks where traffic can advance towards its destination, getting buffered upon encountering contention and resuming transmission once resources are freed. In contrast, PSFs stop the injection of all traffic that generate contention. Consequently, it is important to understand how the order in which flows are serviced affects performance metrics. Our contribution is to quantify this impact through a comprehensive simulation-based evaluation focusing on a recently fabricated PSF prototype. Our experiments include configurations with three routing algorithms, two switching methods, three ToR switch sizes and 9 representative workloads from the DC and HPC domains. We found that the effect of arbitration on raw throughput is negligible but, when considering more realistic loads, selecting an appropriate arbitration policy can improve communication time and energy efficiency. Indeed, the communication time can be reduced by between 10% and 30% by employing appropriate arbitration. Switching energy efficiency can also be improved between 4% and 13%. Finally, insertion loss is barely affected, with differences below 2%. LFU and ARR were found to obtain the best results. LFU is very good with regular workloads but one of the worse with irregular workloads. ARR obtains good results regardless of the type of workload.\n
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\n \n\n \n \n \n \n \n On the parallelization of multipacting simulation codes for the design of particle accelerator components.\n \n \n \n\n\n \n Navaridas, J.; Pascual, J., A.; Galarza, J.; Romero, T.; Muñoz, J., L.; and Bustinduy, I.\n\n\n \n\n\n\n Journal of Supercomputing. 2024.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {On the parallelization of multipacting simulation codes for the design of particle accelerator components},\n type = {article},\n year = {2024},\n id = {c90baeb1-4649-386b-a556-c273e949e24e},\n created = {2024-01-26T12:51:42.836Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2024-01-26T12:51:42.836Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Navaridas, J and Pascual, J A and Galarza, J and Romero, T and Muñoz, J L and Bustinduy, I},\n doi = {10.1007/s11227-024-05896-2},\n journal = {Journal of Supercomputing}\n}
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\n \n\n \n \n \n \n \n \n A fishing route optimization decision support system: The case of the tuna purse seiner.\n \n \n \n \n\n\n \n Granado, I.; Hernando, L.; Uriondo, Z.; and Fernandes-Salvador, J., A.\n\n\n \n\n\n\n European Journal of Operational Research, 312(2): 718-732. 2024.\n \n\n\n\n
\n\n\n\n \n \n \"AWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {A fishing route optimization decision support system: The case of the tuna purse seiner},\n type = {article},\n year = {2024},\n keywords = {Decision support system,Fisheries planning,Genetic algorithm,Route optimization,Time-dependent A*},\n pages = {718-732},\n volume = {312},\n websites = {https://www.sciencedirect.com/science/article/pii/S0377221723005477},\n id = {76eb2de2-3e30-3564-8aa4-4f736692f530},\n created = {2024-02-06T10:43:01.375Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2024-02-06T10:43:01.375Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {Fisheries face challenges in improving efficiency and reducing their emission footprint and operating costs. Decision support systems offer an opportunity to tackle such challenges. This study focuses on the dynamic fishing routing problem (DFRP) of a tuna purse seiner from a tactical and operational routing point of view. The tactical routing problem is formalized as the dynamic k-travelling salesperson problem with moving targets and time windows, whereas the operational problem is formulated as the time-dependent shortest path problem. The algorithm proposed to solve this problem, called GA-TDA*, couples a genetic algorithm (GA), which uses problem-dependent operators, with a time-dependent A* algorithm. Using real data from a fishing company, the designed GA crossovers were evaluated along with the trade-off between the combination of the proposed objectives: fuel consumption and probability of high catches. The DFRP was also solved as a real dynamic problem with route updates every time a dFAD was fished. The results obtained by this approach were compared with historical fishing trips, where a potential saving in fuel consumption and time at sea of around 57% and 33%, respectively were shown. The dynamic GA-TDA* shows that a better selection of fishing grounds together with considerations about weather conditions can help industry to mitigate and adapt to climate change while decreasing one of their main operational costs.},\n bibtype = {article},\n author = {Granado, Igor and Hernando, Leticia and Uriondo, Zigor and Fernandes-Salvador, Jose A},\n doi = {https://doi.org/10.1016/j.ejor.2023.07.009},\n journal = {European Journal of Operational Research},\n number = {2}\n}
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\n Fisheries face challenges in improving efficiency and reducing their emission footprint and operating costs. Decision support systems offer an opportunity to tackle such challenges. This study focuses on the dynamic fishing routing problem (DFRP) of a tuna purse seiner from a tactical and operational routing point of view. The tactical routing problem is formalized as the dynamic k-travelling salesperson problem with moving targets and time windows, whereas the operational problem is formulated as the time-dependent shortest path problem. The algorithm proposed to solve this problem, called GA-TDA*, couples a genetic algorithm (GA), which uses problem-dependent operators, with a time-dependent A* algorithm. Using real data from a fishing company, the designed GA crossovers were evaluated along with the trade-off between the combination of the proposed objectives: fuel consumption and probability of high catches. The DFRP was also solved as a real dynamic problem with route updates every time a dFAD was fished. The results obtained by this approach were compared with historical fishing trips, where a potential saving in fuel consumption and time at sea of around 57% and 33%, respectively were shown. The dynamic GA-TDA* shows that a better selection of fishing grounds together with considerations about weather conditions can help industry to mitigate and adapt to climate change while decreasing one of their main operational costs.\n
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\n  \n 2023\n \n \n (27)\n \n \n
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\n \n\n \n \n \n \n \n \n Towards a fair comparison and realistic evaluation framework of android malware detectors based on static analysis and machine learning.\n \n \n \n \n\n\n \n Molina-Coronado, B.; Mori, U.; Mendiburu, A.; and Miguel-Alonso, J.\n\n\n \n\n\n\n Computers & Security, 124: 102996. 2023.\n \n\n\n\n
\n\n\n\n \n \n \"TowardsWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Towards a fair comparison and realistic evaluation framework of android malware detectors based on static analysis and machine learning},\n type = {article},\n year = {2023},\n pages = {102996},\n volume = {124},\n websites = {https://www.sciencedirect.com/science/article/pii/S0167404822003881},\n id = {bfea1837-3974-324b-968c-49d14d85e675},\n created = {2022-11-07T09:01:27.781Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2022-11-07T09:01:27.781Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Molina-Coronado, Borja and Mori, Usue and Mendiburu, Alexander and Miguel-Alonso, Jose},\n doi = {https://doi.org/10.1016/j.cose.2022.102996},\n journal = {Computers & Security}\n}
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\n \n\n \n \n \n \n \n A Research Review of OpenFlow for Datacenter Networking.\n \n \n \n\n\n \n Miguel-Alonso, J.\n\n\n \n\n\n\n IEEE Access, 11: 770-786. 2023.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {A Research Review of OpenFlow for Datacenter Networking},\n type = {article},\n year = {2023},\n pages = {770-786},\n volume = {11},\n id = {5c060d08-415b-35d7-8a9a-93399be09741},\n created = {2023-01-12T09:10:10.852Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2023-01-12T09:10:10.852Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {ARTICLE},\n private_publication = {false},\n bibtype = {article},\n author = {Miguel-Alonso, Jose},\n doi = {10.1109/ACCESS.2022.3233466},\n journal = {IEEE Access}\n}
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\n \n\n \n \n \n \n \n \n Extending Adversarial Attacks to Produce Adversarial Class Probability Distributions.\n \n \n \n \n\n\n \n Vadillo, J.; Santana, R.; and Lozano, J., A.\n\n\n \n\n\n\n Journal of Machine Learning Research, 24(15): 1-42. 2023.\n \n\n\n\n
\n\n\n\n \n \n \"ExtendingWebsite\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Extending Adversarial Attacks to Produce Adversarial Class Probability Distributions},\n type = {article},\n year = {2023},\n pages = {1-42},\n volume = {24},\n websites = {http://jmlr.org/papers/v24/21-0326.html},\n id = {64fa8621-0c50-3d07-be9c-53e247f07f25},\n created = {2023-01-23T08:45:02.541Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2023-01-23T08:45:02.541Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Vadillo, Jon and Santana, Roberto and Lozano, Jose A},\n journal = {Journal of Machine Learning Research},\n number = {15}\n}
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\n \n\n \n \n \n \n \n Parallelizing Multipacting Simulation for the Design of Particle Accelerator Components.\n \n \n \n\n\n \n Galarza, J.; Navaridas, J.; Pascual, J., A.; Muñoz, J., L.; Bustinduy, I.; and Romero, T.\n\n\n \n\n\n\n In 31st Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, of PDP '23, 3 2023. Euromicro\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Parallelizing Multipacting Simulation for the Design of Particle Accelerator Components},\n type = {inproceedings},\n year = {2023},\n month = {3},\n publisher = {Euromicro},\n series = {PDP '23},\n id = {7715e083-6346-3456-b0fd-ca90b47093e5},\n created = {2023-05-02T07:53:24.180Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2023-05-02T07:53:24.180Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Galarza, Julen and Navaridas, Javier and Pascual, Jose A and Muñoz, Juan L and Bustinduy, Ibon and Romero, Txomin},\n booktitle = {31st Euromicro International Conference on Parallel, Distributed, and Network-Based Processing}\n}
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\n \n\n \n \n \n \n \n \n Trajectory optimization of space vehicle in rendezvous proximity operation with evolutionary feasibility conserving techniques.\n \n \n \n \n\n\n \n Shirazi, A.; Ceberio, J.; and Lozano, J., A.\n\n\n \n\n\n\n Engineering Applications of Artificial Intelligence, 117: 105523. 2023.\n \n\n\n\n
\n\n\n\n \n \n \"TrajectoryWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Trajectory optimization of space vehicle in rendezvous proximity operation with evolutionary feasibility conserving techniques},\n type = {article},\n year = {2023},\n keywords = {Estimation of distribution algorithms,Proximity operation,Satellite rendezvous,Space transportation,Trajectory optimization},\n pages = {105523},\n volume = {117},\n websites = {https://www.sciencedirect.com/science/article/pii/S0952197622005139},\n id = {c1f75ed4-ab99-318e-a801-bf0141418920},\n created = {2023-05-23T15:20:53.907Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2023-05-23T15:20:53.907Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {In this paper, a direct approach is developed for discovering optimal transfer trajectories of close-range rendezvous of satellites considering disturbances in elliptical orbits. The control vector representing the inputs is parameterized via different interpolation methods, and an Estimation of Distribution Algorithm (EDA) that implements mixtures of probability models is presented. To satisfy the terminal conditions, which are represented as non-linear inequality constraints, several feasibility conserving mechanisms associated with learning and sampling methods of the EDAs are proposed, which guarantee the feasibility of the explored solutions. They include a particular implementation of a clustering algorithm, outlier detection, and several heuristic mapping methods. The combination of the proposed operators guides the optimization process in achieving the optimal solution by surfing the regions of the search domain associated with feasible solutions. Numerical simulations confirm that space transfer trajectories with minimum-fuel consumption for the chaser spacecraft can be obtained with terminal condition satisfaction in rendezvous proximity operation.},\n bibtype = {article},\n author = {Shirazi, Abolfazl and Ceberio, Josu and Lozano, Jose A},\n doi = {https://doi.org/10.1016/j.engappai.2022.105523},\n journal = {Engineering Applications of Artificial Intelligence}\n}
\n
\n\n\n
\n In this paper, a direct approach is developed for discovering optimal transfer trajectories of close-range rendezvous of satellites considering disturbances in elliptical orbits. The control vector representing the inputs is parameterized via different interpolation methods, and an Estimation of Distribution Algorithm (EDA) that implements mixtures of probability models is presented. To satisfy the terminal conditions, which are represented as non-linear inequality constraints, several feasibility conserving mechanisms associated with learning and sampling methods of the EDAs are proposed, which guarantee the feasibility of the explored solutions. They include a particular implementation of a clustering algorithm, outlier detection, and several heuristic mapping methods. The combination of the proposed operators guides the optimization process in achieving the optimal solution by surfing the regions of the search domain associated with feasible solutions. Numerical simulations confirm that space transfer trajectories with minimum-fuel consumption for the chaser spacecraft can be obtained with terminal condition satisfaction in rendezvous proximity operation.\n
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\n \n\n \n \n \n \n \n \n Delineation of site-specific management zones using estimation of distribution algorithms.\n \n \n \n \n\n\n \n Velasco, J.; Vicencio, S.; Lozano, J., A.; and Cid-Garcia, N., M.\n\n\n \n\n\n\n International Transactions in Operational Research, 30(4): 1703-1729. 2023.\n \n\n\n\n
\n\n\n\n \n \n \"DelineationWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Delineation of site-specific management zones using estimation of distribution algorithms},\n type = {article},\n year = {2023},\n keywords = {combinatorial optimization,estimation of distribution algorithms,evolutionary computation,orthogonal shapes,site-specific management zones},\n pages = {1703-1729},\n volume = {30},\n websites = {https://onlinelibrary.wiley.com/doi/abs/10.1111/itor.12970},\n id = {d81d36b7-f3f6-3495-b47c-4df022be2e1b},\n created = {2023-05-23T15:20:56.774Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2023-05-23T15:20:56.774Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {Abstract In this paper, we present a novel methodology to solve the problem of delineating homogeneous site-specific management zones (SSMZ) in agricultural fields. This problem consists of dividing the field into small regions for which a specific rate of inputs is required. The objective is to minimize the number of management zones, which must be homogeneous according to a specific soil property: physical or chemical. Furthermore, as opposed to oval zones, SSMZ with rectangular shapes are preferable since they are more practical for agricultural technologies. The methodology we propose is based on evolutionary computation, specifically on a class of the estimation of distribution algorithms (EDAs). One of the strongest contributions of this study is the representation used to model the management zones, which generates zones with orthogonal shapes, for example, L or T shapes, and minimizes the number of zones required to delineate the field. The experimental results show that our method is efficient to solve real field and randomly generated instances. The average improvement of our method consists in reducing the number of management zones in the agricultural fields concerning other operations research methods presented in the literature. The improvement depends on the size of the field and the level of homogeneity established for the resulting management zones.},\n bibtype = {article},\n author = {Velasco, Jonas and Vicencio, Salvador and Lozano, Jose A and Cid-Garcia, Nestor M},\n doi = {https://doi.org/10.1111/itor.12970},\n journal = {International Transactions in Operational Research},\n number = {4}\n}
\n
\n\n\n
\n Abstract In this paper, we present a novel methodology to solve the problem of delineating homogeneous site-specific management zones (SSMZ) in agricultural fields. This problem consists of dividing the field into small regions for which a specific rate of inputs is required. The objective is to minimize the number of management zones, which must be homogeneous according to a specific soil property: physical or chemical. Furthermore, as opposed to oval zones, SSMZ with rectangular shapes are preferable since they are more practical for agricultural technologies. The methodology we propose is based on evolutionary computation, specifically on a class of the estimation of distribution algorithms (EDAs). One of the strongest contributions of this study is the representation used to model the management zones, which generates zones with orthogonal shapes, for example, L or T shapes, and minimizes the number of zones required to delineate the field. The experimental results show that our method is efficient to solve real field and randomly generated instances. The average improvement of our method consists in reducing the number of management zones in the agricultural fields concerning other operations research methods presented in the literature. The improvement depends on the size of the field and the level of homogeneity established for the resulting management zones.\n
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\n \n\n \n \n \n \n \n \n Introducing multi-dimensional hierarchical classification: Characterization, solving strategies and performance measures.\n \n \n \n \n\n\n \n Montenegro, C.; Santana, R.; and Lozano, J., A.\n\n\n \n\n\n\n Neurocomputing, 533: 141-160. 2023.\n \n\n\n\n
\n\n\n\n \n \n \"IntroducingWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Introducing multi-dimensional hierarchical classification: Characterization, solving strategies and performance measures},\n type = {article},\n year = {2023},\n keywords = {Hierarchical classification,Machine learning,Multi-dimensional classification,Multi-dimensional hierarchical classification},\n pages = {141-160},\n volume = {533},\n websites = {https://www.sciencedirect.com/science/article/pii/S0925231223001984},\n id = {a917b55a-e94d-325e-b314-0a007b6f44c1},\n created = {2023-05-23T15:20:57.090Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2023-05-23T15:20:57.090Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {Classification problems where there exist multiple class variables that need to be jointly predicted are known as Multi-dimensional classification problems. If the labels of these class variables are organized as hierarchies, we can take advantage of specific strategies designed for the Hierarchical classification paradigm. In this paper we present the Multi-dimensional hierarchical classification (MDHC) paradigm, a result of the combination of Multi-dimensional and Hierarchical classification paradigms. We propose four MDHC learning strategies which are designed to exploit the particularities of this new paradigm, combining characteristics of Multi-dimensional and Hierarchical classification strategies. Along with these strategies, we present a framework for classifier comparison in which we use a set of performance measures specifically designed for MDHC, and a procedure to create MDHC synthetic scenarios. Using this framework and the performance measures presented, we study how characteristics of the MDHC problems influence the performance of the different MDHC strategies proposed, and compare them to other non-MDHC strategies.},\n bibtype = {article},\n author = {Montenegro, C and Santana, R and Lozano, J A},\n doi = {https://doi.org/10.1016/j.neucom.2023.02.050},\n journal = {Neurocomputing}\n}
\n
\n\n\n
\n Classification problems where there exist multiple class variables that need to be jointly predicted are known as Multi-dimensional classification problems. If the labels of these class variables are organized as hierarchies, we can take advantage of specific strategies designed for the Hierarchical classification paradigm. In this paper we present the Multi-dimensional hierarchical classification (MDHC) paradigm, a result of the combination of Multi-dimensional and Hierarchical classification paradigms. We propose four MDHC learning strategies which are designed to exploit the particularities of this new paradigm, combining characteristics of Multi-dimensional and Hierarchical classification strategies. Along with these strategies, we present a framework for classifier comparison in which we use a set of performance measures specifically designed for MDHC, and a procedure to create MDHC synthetic scenarios. Using this framework and the performance measures presented, we study how characteristics of the MDHC problems influence the performance of the different MDHC strategies proposed, and compare them to other non-MDHC strategies.\n
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\n \n\n \n \n \n \n \n \n Characterizing Permutation-Based Combinatorial Optimization Problems in Fourier Space.\n \n \n \n \n\n\n \n Elorza, A.; Hernando, L.; and Lozano, J., A.\n\n\n \n\n\n\n Evolutionary Computation,1-37. 5 2023.\n \n\n\n\n
\n\n\n\n \n \n \"CharacterizingWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Characterizing Permutation-Based Combinatorial Optimization Problems in Fourier Space},\n type = {article},\n year = {2023},\n pages = {1-37},\n websites = {https://doi.org/10.1162/evco_a_00315},\n month = {5},\n id = {87d6e3a5-2626-3a17-9ede-920d271f8725},\n created = {2023-05-23T15:20:57.699Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2023-05-23T15:20:57.699Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {Comparing combinatorial optimization problems is a difficult task. They are defined using different criteria and terms: weights, flows, distances, etc. In spite of this apparent discrepancy, on many occasions, they tend to produce problem instances with similar properties. One avenue to compare different problems is to project them onto the same space, in order to have homogeneous representations. Expressing the problems in a unified framework could also lead to the discovery of theoretical properties or the design of new algorithms. This article proposes the use of the Fourier transform over the symmetric group as the tool to project different permutation-based combinatorial optimization problems onto the same space. Based on a previous study (Kondor, 2010), which characterized the Fourier coefficients of the quadratic assignment problem, we describe the Fourier coefficients of three other well-known problems: the symmetric and nonsymmetric traveling salesperson problem and the linear ordering problem. This transformation allows us to gain a better understanding of the intersection between the problems, as well as to bound their intrinsic dimension.},\n bibtype = {article},\n author = {Elorza, Anne and Hernando, Leticia and Lozano, Jose A},\n doi = {10.1162/evco_a_00315},\n journal = {Evolutionary Computation}\n}
\n
\n\n\n
\n Comparing combinatorial optimization problems is a difficult task. They are defined using different criteria and terms: weights, flows, distances, etc. In spite of this apparent discrepancy, on many occasions, they tend to produce problem instances with similar properties. One avenue to compare different problems is to project them onto the same space, in order to have homogeneous representations. Expressing the problems in a unified framework could also lead to the discovery of theoretical properties or the design of new algorithms. This article proposes the use of the Fourier transform over the symmetric group as the tool to project different permutation-based combinatorial optimization problems onto the same space. Based on a previous study (Kondor, 2010), which characterized the Fourier coefficients of the quadratic assignment problem, we describe the Fourier coefficients of three other well-known problems: the symmetric and nonsymmetric traveling salesperson problem and the linear ordering problem. This transformation allows us to gain a better understanding of the intersection between the problems, as well as to bound their intrinsic dimension.\n
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\n \n\n \n \n \n \n \n Selective Imputation for Multivariate Time Series Datasets with Missing Values.\n \n \n \n\n\n \n Blázquez-Garc\\'\\ia, A.; Wickstrøm, K.; Yu, S.; Øyvind Mikalsen, K.; Boubekki, A.; Conde, A.; Mori, U.; Jenssen, R.; and Lozano, J., A.\n\n\n \n\n\n\n IEEE Transactions on Knowledge and Data Engineering,1-12. 2023.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Selective Imputation for Multivariate Time Series Datasets with Missing Values},\n type = {article},\n year = {2023},\n pages = {1-12},\n id = {179cdd1e-89e6-3f1f-bbd1-e64cdddc1b27},\n created = {2023-05-23T15:20:57.964Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2023-05-23T15:20:57.964Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Blázquez-Garc\\'\\ia, Ane and Wickstrøm, Kristoffer and Yu, Shujian and Øyvind Mikalsen, Karl and Boubekki, Ahcene and Conde, Angel and Mori, Usue and Jenssen, Robert and Lozano, Jose A},\n doi = {10.1109/TKDE.2023.3240858},\n journal = {IEEE Transactions on Knowledge and Data Engineering}\n}
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\n \n\n \n \n \n \n \n Minimum Recall-based Loss Function for Imbalanced Time Series Classification.\n \n \n \n\n\n \n Ircio, J.; Lojo, A.; Mori, U.; Malinowski, S.; and Lozano, J., A.\n\n\n \n\n\n\n IEEE Transactions on Knowledge and Data Engineering,1-11. 2023.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Minimum Recall-based Loss Function for Imbalanced Time Series Classification},\n type = {article},\n year = {2023},\n pages = {1-11},\n id = {f9bf198e-d413-31bd-87d5-08f8e3970511},\n created = {2023-05-23T15:20:58.239Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2023-05-23T15:20:58.239Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Ircio, Josu and Lojo, Aizea and Mori, Usue and Malinowski, Simon and Lozano, Jose A},\n doi = {10.1109/TKDE.2023.3268994},\n journal = {IEEE Transactions on Knowledge and Data Engineering}\n}
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\n \n\n \n \n \n \n \n \n Fast computation of cluster validity measures for bregman divergences and benefits.\n \n \n \n \n\n\n \n Capó, M.; Pérez, A.; and Lozano, J., A.\n\n\n \n\n\n\n Pattern Recognition Letters, 170: 100-105. 2023.\n \n\n\n\n
\n\n\n\n \n \n \"FastWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Fast computation of cluster validity measures for bregman divergences and benefits},\n type = {article},\n year = {2023},\n keywords = {Bregman divergences,Caliński-Harabasz,Davies-Bouldin,Number of clusters,Partitional clustering,Silhouette index},\n pages = {100-105},\n volume = {170},\n websites = {https://www.sciencedirect.com/science/article/pii/S0167865523001290},\n id = {0649bd91-1945-358d-b626-f02b825e7e49},\n created = {2023-05-23T15:20:58.534Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2023-05-23T15:20:58.534Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {Partitional clustering is one of the most relevant unsupervised learning and pattern recognition techniques. Unfortunately, one of the main drawbacks of these methodologies refer to the fact that the number of clusters is generally assumed to be known beforehand and automating its selection is not straightforward. On the same token, internal validity measures, such as the Silhouette index, Davies-Bouldin and Caliski-Harabasz measures have emerged as the standard techniques to be used when comparing the goodness of clustering results obtained via different clustering methods. These measures take into consideration both the inter and intra-cluster simmilarities and can be adapted to different metrics. Unfortunately, their used has been hindered due to their large computational complexities, which are commonly quadratic with respect to the number of instances of the data set. In this work, we show that the time complexity of computing the most popular internal validity measures can be utterly reduced by making used of the within-cluster errors and different properties of the Bregman divergences. This contribution ultimately allows us to massively speed-up the selection of an adequate number of clusters for a given data set as verified with extensive empirical comparisons.},\n bibtype = {article},\n author = {Capó, Marco and Pérez, Aritz and Lozano, Jose A},\n doi = {https://doi.org/10.1016/j.patrec.2023.05.001},\n journal = {Pattern Recognition Letters}\n}
\n
\n\n\n
\n Partitional clustering is one of the most relevant unsupervised learning and pattern recognition techniques. Unfortunately, one of the main drawbacks of these methodologies refer to the fact that the number of clusters is generally assumed to be known beforehand and automating its selection is not straightforward. On the same token, internal validity measures, such as the Silhouette index, Davies-Bouldin and Caliski-Harabasz measures have emerged as the standard techniques to be used when comparing the goodness of clustering results obtained via different clustering methods. These measures take into consideration both the inter and intra-cluster simmilarities and can be adapted to different metrics. Unfortunately, their used has been hindered due to their large computational complexities, which are commonly quadratic with respect to the number of instances of the data set. In this work, we show that the time complexity of computing the most popular internal validity measures can be utterly reduced by making used of the within-cluster errors and different properties of the Bregman divergences. This contribution ultimately allows us to massively speed-up the selection of an adequate number of clusters for a given data set as verified with extensive empirical comparisons.\n
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\n \n\n \n \n \n \n \n \n On the elusivity of dynamic optimisation problems.\n \n \n \n \n\n\n \n Alza, J.; Bartlett, M.; Ceberio, J.; and McCall, J.\n\n\n \n\n\n\n Swarm and Evolutionary Computation, 78: 101289. 2023.\n \n\n\n\n
\n\n\n\n \n \n \"OnWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {On the elusivity of dynamic optimisation problems},\n type = {article},\n year = {2023},\n keywords = {Adaptative advantage,Dynamic optimisation problem,Elusivity,Online solving,Restart},\n pages = {101289},\n volume = {78},\n websites = {https://www.sciencedirect.com/science/article/pii/S2210650223000627},\n id = {903d43ce-d964-37f8-94b0-d6659993c1c7},\n created = {2023-05-23T15:20:58.825Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2023-05-23T15:20:58.825Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {The field of dynamic optimisation continuously designs and compares algorithms with adaptation abilities that deal with changing problems during their search process. However, restarting the search algorithm after a detected change is sometimes a better option than adaptation, although it is generally ignored in empirical studies. In this paper, we suggest the elusivity formulation to (i) quantify the preference for restart over adaptation for algorithms running on dynamic problems, and (ii) evaluate the advantage and behaviour of adaptation. Informally, we state that a dynamic problem is elusive to an algorithm if restart is more effective than adapting to changes. After reviewing existing formalisms for dynamic optimisation, the elusivity concept is mathematically defined and applied to two published empirical studies to evaluate its utility. Conducted experiments show that replicated works include elusive problems, where restart is better than (or equal to) adaptation, and demonstrate that some empirical research effort is being devoted to evaluating adaptive algorithms in circumstances where there is no advantage. Hence, we recommend how and when elusivity analysis can be gainfully included in empirical studies in the field of dynamic optimisation.},\n bibtype = {article},\n author = {Alza, Joan and Bartlett, Mark and Ceberio, Josu and McCall, John},\n doi = {https://doi.org/10.1016/j.swevo.2023.101289},\n journal = {Swarm and Evolutionary Computation}\n}
\n
\n\n\n
\n The field of dynamic optimisation continuously designs and compares algorithms with adaptation abilities that deal with changing problems during their search process. However, restarting the search algorithm after a detected change is sometimes a better option than adaptation, although it is generally ignored in empirical studies. In this paper, we suggest the elusivity formulation to (i) quantify the preference for restart over adaptation for algorithms running on dynamic problems, and (ii) evaluate the advantage and behaviour of adaptation. Informally, we state that a dynamic problem is elusive to an algorithm if restart is more effective than adapting to changes. After reviewing existing formalisms for dynamic optimisation, the elusivity concept is mathematically defined and applied to two published empirical studies to evaluate its utility. Conducted experiments show that replicated works include elusive problems, where restart is better than (or equal to) adaptation, and demonstrate that some empirical research effort is being devoted to evaluating adaptive algorithms in circumstances where there is no advantage. Hence, we recommend how and when elusivity analysis can be gainfully included in empirical studies in the field of dynamic optimisation.\n
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\n \n\n \n \n \n \n \n \n New Knowledge about the Elementary Landscape Decomposition for Solving the Quadratic Assignment Problem.\n \n \n \n \n\n\n \n Benavides, X.; Ceberio, J.; Hernando, L.; and Lozano, J., A.\n\n\n \n\n\n\n In Proceedings of the Genetic and Evolutionary Computation Conference, of GECCO '23, pages 239-247, 2023. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"NewWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {New Knowledge about the Elementary Landscape Decomposition for Solving the Quadratic Assignment Problem},\n type = {inproceedings},\n year = {2023},\n keywords = {elementary landscapes,quadratic assignment problem},\n pages = {239-247},\n websites = {https://doi.org/10.1145/3583131.3590369},\n publisher = {Association for Computing Machinery},\n city = {New York, NY, USA},\n series = {GECCO '23},\n id = {b6caa57e-5650-3038-b20b-552a75a222fa},\n created = {2024-01-09T11:01:04.211Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2024-01-09T11:01:04.211Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {Previous works have shown that studying the characteristics of the Quadratic Assignment Problem (QAP) is a crucial step in gaining knowledge that can be used to design tailored meta-heuristic algorithms. One way to analyze the characteristics of the QAP is to decompose its objective function into a linear combination of orthogonal sub-functions that can be independently studied. In particular, this work focuses on a decomposition approach that has attracted considerable attention: the Elementary Landscape Decomposition (ELD).The main drawback of the ELD is that it does not allow an understandable characterization of what is being measured by each component of the decomposition. Thus, it turns out difficult to design new efficient meta-heuristic algorithms for the QAP based on the ELD. To address this issue, in this work, we delve deeper into the ELD by means of an additional decomposition of its elementary components. Conducted experiments show that the performed analysis may be used to explain the behaviour of ELD-based methods, providing critical information about their potential applications.},\n bibtype = {inproceedings},\n author = {Benavides, Xabier and Ceberio, Josu and Hernando, Leticia and Lozano, Jose Antonio},\n doi = {10.1145/3583131.3590369},\n booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference}\n}
\n
\n\n\n
\n Previous works have shown that studying the characteristics of the Quadratic Assignment Problem (QAP) is a crucial step in gaining knowledge that can be used to design tailored meta-heuristic algorithms. One way to analyze the characteristics of the QAP is to decompose its objective function into a linear combination of orthogonal sub-functions that can be independently studied. In particular, this work focuses on a decomposition approach that has attracted considerable attention: the Elementary Landscape Decomposition (ELD).The main drawback of the ELD is that it does not allow an understandable characterization of what is being measured by each component of the decomposition. Thus, it turns out difficult to design new efficient meta-heuristic algorithms for the QAP based on the ELD. To address this issue, in this work, we delve deeper into the ELD by means of an additional decomposition of its elementary components. Conducted experiments show that the performed analysis may be used to explain the behaviour of ELD-based methods, providing critical information about their potential applications.\n
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\n \n\n \n \n \n \n \n \n Early prediction of Ibex 35 movements.\n \n \n \n \n\n\n \n Miranda García, I., M.; Segovia-Vargas, M.; Mori, U.; and Lozano, J., A.\n\n\n \n\n\n\n Journal of Forecasting, 42(5): 1150-1166. 2023.\n \n\n\n\n
\n\n\n\n \n \n \"EarlyWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Early prediction of Ibex 35 movements},\n type = {article},\n year = {2023},\n keywords = {artificial intelligence,high-frequency data,intraday pattern,price discovery,stock price prediction,trading hours},\n pages = {1150-1166},\n volume = {42},\n websites = {https://onlinelibrary.wiley.com/doi/abs/10.1002/for.2933},\n id = {f53013f6-d1d6-3903-92dc-cdc1f0b26d5d},\n created = {2024-01-22T09:10:50.792Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2024-01-22T09:10:50.792Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {Abstract In this paper, we examine the early predictability of the market's directional movement using intraday high-frequency data (695,764 observations) from an stock index (Ibex 35 Index) to provide, either private or institutional investors, an early warning system based on an “early indicator” of the financial market fluctuations with an optimal combination of the two more relevant variables for this strategy, accuracy, and earliness. A novel supervised machine learning early classification technique (Artificial Intelligence) has been applied, for the first time, to the high-frequency time series of both price and certain technical indicators. The results obtained allow us to assert that the intraday movement of the Ibex 35 can be predicted with acceptable levels of accuracy 24 min after the start of the session and to establish certain informative intraday hourly patterns. Consequently, different indicators of precision and earliness in the session are generated, obtaining that, after a certain point in the session, no gains in precision are generated.},\n bibtype = {article},\n author = {Miranda García, I Marta and Segovia-Vargas, María-Jesús and Mori, Usue and Lozano, José A},\n doi = {https://doi.org/10.1002/for.2933},\n journal = {Journal of Forecasting},\n number = {5}\n}
\n
\n\n\n
\n Abstract In this paper, we examine the early predictability of the market's directional movement using intraday high-frequency data (695,764 observations) from an stock index (Ibex 35 Index) to provide, either private or institutional investors, an early warning system based on an “early indicator” of the financial market fluctuations with an optimal combination of the two more relevant variables for this strategy, accuracy, and earliness. A novel supervised machine learning early classification technique (Artificial Intelligence) has been applied, for the first time, to the high-frequency time series of both price and certain technical indicators. The results obtained allow us to assert that the intraday movement of the Ibex 35 can be predicted with acceptable levels of accuracy 24 min after the start of the session and to establish certain informative intraday hourly patterns. Consequently, different indicators of precision and earliness in the session are generated, obtaining that, after a certain point in the session, no gains in precision are generated.\n
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\n \n\n \n \n \n \n \n Fast K-Medoids With the l\\_1-Norm.\n \n \n \n\n\n \n Capó, M.; Pérez, A.; and Lozano, J., A.\n\n\n \n\n\n\n IEEE Transactions on Artificial Intelligence,1-14. 2023.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Fast K-Medoids With the l\\_1-Norm},\n type = {article},\n year = {2023},\n pages = {1-14},\n id = {efbb2c4b-4d06-3259-ab8b-a877c8656cd0},\n created = {2024-01-22T09:12:51.639Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2024-01-22T09:12:51.639Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {ARTICLE},\n private_publication = {false},\n bibtype = {article},\n author = {Capó, Marco and Pérez, Aritz and Lozano, Jose A},\n doi = {10.1109/TAI.2023.3298752},\n journal = {IEEE Transactions on Artificial Intelligence}\n}
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\n \n\n \n \n \n \n \n \n On the Use of Second Order Neighbors to Escape from Local Optima.\n \n \n \n \n\n\n \n Torralbo, M.; Hernando, L.; Contreras-Torres, E.; and Lozano, J., A.\n\n\n \n\n\n\n In Proceedings of the Genetic and Evolutionary Computation Conference, of GECCO '23, pages 375-383, 2023. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"OnWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {On the Use of Second Order Neighbors to Escape from Local Optima},\n type = {inproceedings},\n year = {2023},\n keywords = {escaping local optima,linear ordering problem,permutation-based combinatorial optimization problems,quadratic assignment problem,second order neighborhood,travelling salesperson problem},\n pages = {375-383},\n websites = {https://doi.org/10.1145/3583131.3590473},\n publisher = {Association for Computing Machinery},\n city = {New York, NY, USA},\n series = {GECCO '23},\n id = {0bc394af-c339-31b5-a77d-eba17403f766},\n created = {2024-01-22T09:14:06.509Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2024-01-22T09:14:06.509Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {Designing efficient local search based algorithms requires to consider the specific properties of the problems. We introduce a simple and efficient strategy, the Extended Reach, that escapes from local optima obtained from a best improvement local search and apply it to the linear ordering problem (LOP), the traveling salesperson problem (TSP) and the quadratic assignment problem (QAP). This strategy is based on two landscape properties observed in the literature. First, it considers that a local optimum is usually located in the frontier of its own attraction basin, and thus, it is enough to inspect the second order neighbors to reach a (better) solution inside an attraction basin of a better local optimum. Second, taking into account that for the LOP and specific neighborhoods it is possible to discard solutions without the need of being evaluated, we extend this result to the TSP with the 2-opt neighborhood to avoid the unnecessary evaluation of solutions. Efficient ways of evaluating the second order neighbors are also presented, based on the cost differences, reducing significantly the computation cost. Experimental results on random and benchmark instances show that our strategy, indeed, escapes from local optima despite its simplicity.},\n bibtype = {inproceedings},\n author = {Torralbo, Manuel and Hernando, Leticia and Contreras-Torres, Ernesto and Lozano, Jose A},\n doi = {10.1145/3583131.3590473},\n booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference}\n}
\n
\n\n\n
\n Designing efficient local search based algorithms requires to consider the specific properties of the problems. We introduce a simple and efficient strategy, the Extended Reach, that escapes from local optima obtained from a best improvement local search and apply it to the linear ordering problem (LOP), the traveling salesperson problem (TSP) and the quadratic assignment problem (QAP). This strategy is based on two landscape properties observed in the literature. First, it considers that a local optimum is usually located in the frontier of its own attraction basin, and thus, it is enough to inspect the second order neighbors to reach a (better) solution inside an attraction basin of a better local optimum. Second, taking into account that for the LOP and specific neighborhoods it is possible to discard solutions without the need of being evaluated, we extend this result to the TSP with the 2-opt neighborhood to avoid the unnecessary evaluation of solutions. Efficient ways of evaluating the second order neighbors are also presented, based on the cost differences, reducing significantly the computation cost. Experimental results on random and benchmark instances show that our strategy, indeed, escapes from local optima despite its simplicity.\n
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\n \n\n \n \n \n \n \n \n Neuroevolutionary algorithms driven by neuron coverage metrics for semi-supervised classification.\n \n \n \n \n\n\n \n Santana, R.; Hidago-Cenalmor, I.; Garciarena, U.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n In Proceedings of the Companion Conference on Genetic and Evolutionary Computation, of GECCO '23 Companion, pages 679-682, 2023. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"NeuroevolutionaryWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Neuroevolutionary algorithms driven by neuron coverage metrics for semi-supervised classification},\n type = {inproceedings},\n year = {2023},\n keywords = {NAS,neuroevolution,neuron coverage,semi-supervised learning},\n pages = {679-682},\n websites = {https://doi.org/10.1145/3583133.3590684},\n publisher = {Association for Computing Machinery},\n city = {New York, NY, USA},\n series = {GECCO '23 Companion},\n id = {54662f82-7ac0-3dce-b7dd-96cc7a68c811},\n created = {2024-01-22T09:15:02.632Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2024-01-22T09:15:02.632Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {In this paper, we address the question of how to evolve neural networks for semi-supervised problems. We introduce neuroevolutionary approaches that exploit unlabeled instances by using neuron coverage metrics computed on the neural network architecture encoded by each candidate solution. In our neuroevolutionary approach, we define fitness functions that combine classification accuracy computed on labeled examples and neuron coverage metrics evaluated using unlabeled examples. Our results show that the use of neuron coverage metrics helps neuroevolution to become less sensitive to the scarcity of labeled data, and can lead in some cases to a more robust generalization of the learned classifiers.},\n bibtype = {inproceedings},\n author = {Santana, Roberto and Hidago-Cenalmor, Iván and Garciarena, Unai and Mendiburu, Alexander and Lozano, Jose Antonio},\n doi = {10.1145/3583133.3590684},\n booktitle = {Proceedings of the Companion Conference on Genetic and Evolutionary Computation}\n}
\n
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\n In this paper, we address the question of how to evolve neural networks for semi-supervised problems. We introduce neuroevolutionary approaches that exploit unlabeled instances by using neuron coverage metrics computed on the neural network architecture encoded by each candidate solution. In our neuroevolutionary approach, we define fitness functions that combine classification accuracy computed on labeled examples and neuron coverage metrics evaluated using unlabeled examples. Our results show that the use of neuron coverage metrics helps neuroevolution to become less sensitive to the scarcity of labeled data, and can lead in some cases to a more robust generalization of the learned classifiers.\n
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\n \n\n \n \n \n \n \n An Improved Version of MMOEA/DC Based on Alternative Clustering Definitions.\n \n \n \n\n\n \n Senhaji, K.; Coello, C., A., C.; and Lozano, J., A.\n\n\n \n\n\n\n In 2023 IEEE Congress on Evolutionary Computation (CEC), pages 1-9, 2023. \n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {An Improved Version of MMOEA/DC Based on Alternative Clustering Definitions},\n type = {inproceedings},\n year = {2023},\n pages = {1-9},\n id = {c2ad5d5b-35bb-31f9-b02a-c136889c0e31},\n created = {2024-01-22T09:17:11.152Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2024-01-22T09:17:11.152Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {INPROCEEDINGS},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Senhaji, Kaoutar and Coello, Carlos A Coello and Lozano, José Antonio},\n doi = {10.1109/CEC53210.2023.10254015},\n booktitle = {2023 IEEE Congress on Evolutionary Computation (CEC)}\n}
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\n \n\n \n \n \n \n \n The Natural Bias of Artificial Instances.\n \n \n \n\n\n \n Unanue, I.; Merino, M.; and Lozano, J., A.\n\n\n \n\n\n\n In 2023 IEEE Congress on Evolutionary Computation (CEC), pages 1-8, 2023. \n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {The Natural Bias of Artificial Instances},\n type = {inproceedings},\n year = {2023},\n pages = {1-8},\n id = {2564fc6d-c8c1-3778-81de-e6071031120f},\n created = {2024-01-22T09:17:50.044Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2024-01-22T09:17:50.044Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {INPROCEEDINGS},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Unanue, Imanol and Merino, María and Lozano, Jose A},\n doi = {10.1109/CEC53210.2023.10254020},\n booktitle = {2023 IEEE Congress on Evolutionary Computation (CEC)}\n}
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\n \n\n \n \n \n \n \n Evolved Neural Networks for Building Energy Prediction.\n \n \n \n\n\n \n Santana, R.; Prol-Godoy, I.; Picallo-Pérez, A.; and Inza, I.\n\n\n \n\n\n\n In 2023 IEEE Symposium Series on Computational Intelligence (SSCI), pages 801-806, 2023. \n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Evolved Neural Networks for Building Energy Prediction},\n type = {inproceedings},\n year = {2023},\n pages = {801-806},\n id = {536bd112-c6fc-3600-b50e-15f600a0c61a},\n created = {2024-01-22T09:20:24.218Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2024-01-22T09:20:24.218Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {INPROCEEDINGS},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Santana, Roberto and Prol-Godoy, Irati and Picallo-Pérez, Ana and Inza, Iñaki},\n doi = {10.1109/SSCI52147.2023.10371897},\n booktitle = {2023 IEEE Symposium Series on Computational Intelligence (SSCI)}\n}
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\n \n\n \n \n \n \n \n The Impact of Imputation Methods on the Classification of Household Devices from Electricity Usage Time Series.\n \n \n \n\n\n \n Santana, R.\n\n\n \n\n\n\n In 2023 Tenth International Conference on Social Networks Analysis, Management and Security (SNAMS), pages 1-6, 2023. \n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {The Impact of Imputation Methods on the Classification of Household Devices from Electricity Usage Time Series},\n type = {inproceedings},\n year = {2023},\n pages = {1-6},\n id = {f8d5b3f5-da60-3b27-913c-984a3e8dc546},\n created = {2024-01-22T09:21:16.399Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2024-01-22T09:21:16.399Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {INPROCEEDINGS},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Santana, Roberto},\n doi = {10.1109/SNAMS60348.2023.10375473},\n booktitle = {2023 Tenth International Conference on Social Networks Analysis, Management and Security (SNAMS)}\n}
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\n \n\n \n \n \n \n \n Parallelizing Multipacting Simulation for the Design of Particle Accelerator Components.\n \n \n \n\n\n \n Galarza, J.; Navaridas, J.; Pascual, J., A.; Romero, T.; Muñoz, J., L.; and Bustinduy, I.\n\n\n \n\n\n\n In 2023 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), pages 149-153, 2023. \n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Parallelizing Multipacting Simulation for the Design of Particle Accelerator Components},\n type = {inproceedings},\n year = {2023},\n keywords = {Codes;Multicore processing;Scalability;Computational modeling;Europe;Parallel processing;Linear particle accelerator;Multicore systems;Multipactor effect;Parallel programming;Particle simulation},\n pages = {149-153},\n id = {a3bfb129-8347-3f5b-925f-309e0666ddf6},\n created = {2024-01-26T11:07:17.708Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2024-01-26T11:07:17.708Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {INPROCEEDINGS},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Galarza, J and Navaridas, J and Pascual, J A and Romero, T and Muñoz, J L and Bustinduy, I},\n doi = {10.1109/PDP59025.2023.00030},\n booktitle = {2023 31st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)}\n}
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\n \n\n \n \n \n \n \n A Novel Simulation Methodology for Silicon Photonic Switching Fabrics.\n \n \n \n\n\n \n Kynigos, M.; Navaridas, J.; Pascual, J.; and Luján, M.\n\n\n \n\n\n\n In 2023 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), pages 114-123, 2023. \n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {A Novel Simulation Methodology for Silicon Photonic Switching Fabrics},\n type = {inproceedings},\n year = {2023},\n keywords = {Optical switches;Crosstalk;Telecommunication traffic;Silicon photonics;Routing;Control systems;Wavelength division multiplexing;Simulation;Photonic Switching Fabrics;Performance Analysis},\n pages = {114-123},\n id = {ad78d071-d1ba-33b1-84f9-6132255ffb96},\n created = {2024-01-26T11:07:54.595Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2024-01-26T11:07:54.595Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {INPROCEEDINGS},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Kynigos, Markos and Navaridas, Javier and Pascual, Jose and Luján, Mikel},\n doi = {10.1109/ISPASS57527.2023.00020},\n booktitle = {2023 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS)}\n}
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\n \n\n \n \n \n \n \n \n GöwFed: A novel federated network intrusion detection system.\n \n \n \n \n\n\n \n Belenguer, A.; Pascual, J., A.; and Navaridas, J.\n\n\n \n\n\n\n Journal of Network and Computer Applications, 217: 103653. 2023.\n \n\n\n\n
\n\n\n\n \n \n \"GöwFed:Website\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {GöwFed: A novel federated network intrusion detection system},\n type = {article},\n year = {2023},\n keywords = {Federated Learning,Gower distance,Internet of Things,Intrusion Detection Systems},\n pages = {103653},\n volume = {217},\n websites = {https://www.sciencedirect.com/science/article/pii/S1084804523000723},\n id = {df8bb433-6ee9-36da-b6a4-15037c002728},\n created = {2024-01-26T11:08:34.260Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2024-01-26T11:08:34.260Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {Network intrusion detection systems are evolving into intelligent systems that perform data analysis while searching for anomalies in their environment. Indeed, the development of deep learning techniques paved the way to build more complex and effective threat detection models. However, training those models may be computationally infeasible in most Edge or IoT devices. Current approaches rely on powerful centralized servers that receive data from all their parties — violating basic privacy constraints and substantially affecting response times and operational costs due to the huge communication overheads. To mitigate these issues, Federated Learning emerged as a promising approach, where different agents collaboratively train a shared model, without exposing training data to others or requiring a compute-intensive centralized infrastructure. This work presents GöwFed, a novel network threat detection system that combines the usage of Gower Dissimilarity matrices and Federated averaging. Different approaches of GöwFed have been developed based on state-of the-art knowledge: (1) a vanilla version — achieving a median point of [0.888, 0.960] in the PR space and a median accuracy of 0.930; and (2) a version instrumented with an attention mechanism — achieving comparable results when 0.8 of the best performing nodes contribute to the model. Furthermore, each variant has been tested using simulation oriented tools provided by TensorFlow Federated framework. In the same way, a centralized analogous development of the Federated systems is carried out to explore their differences in terms of scalability and performance — the median point of the experiments is [0.987, 0.987]) and the median accuracy is 0.989. Overall, GöwFed intends to be the first stepping stone towards the combined usage of Federated Learning and Gower Dissimilarity matrices to detect network threats in industrial-level networks.},\n bibtype = {article},\n author = {Belenguer, Aitor and Pascual, Jose A and Navaridas, Javier},\n doi = {https://doi.org/10.1016/j.jnca.2023.103653},\n journal = {Journal of Network and Computer Applications}\n}
\n
\n\n\n
\n Network intrusion detection systems are evolving into intelligent systems that perform data analysis while searching for anomalies in their environment. Indeed, the development of deep learning techniques paved the way to build more complex and effective threat detection models. However, training those models may be computationally infeasible in most Edge or IoT devices. Current approaches rely on powerful centralized servers that receive data from all their parties — violating basic privacy constraints and substantially affecting response times and operational costs due to the huge communication overheads. To mitigate these issues, Federated Learning emerged as a promising approach, where different agents collaboratively train a shared model, without exposing training data to others or requiring a compute-intensive centralized infrastructure. This work presents GöwFed, a novel network threat detection system that combines the usage of Gower Dissimilarity matrices and Federated averaging. Different approaches of GöwFed have been developed based on state-of the-art knowledge: (1) a vanilla version — achieving a median point of [0.888, 0.960] in the PR space and a median accuracy of 0.930; and (2) a version instrumented with an attention mechanism — achieving comparable results when 0.8 of the best performing nodes contribute to the model. Furthermore, each variant has been tested using simulation oriented tools provided by TensorFlow Federated framework. In the same way, a centralized analogous development of the Federated systems is carried out to explore their differences in terms of scalability and performance — the median point of the experiments is [0.987, 0.987]) and the median accuracy is 0.989. Overall, GöwFed intends to be the first stepping stone towards the combined usage of Federated Learning and Gower Dissimilarity matrices to detect network threats in industrial-level networks.\n
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\n \n\n \n \n \n \n \n SiliconBurmuin: A Horizon Europe propelled Neurocomputing Initiative in the Basque Country.\n \n \n \n\n\n \n Iturbe, X.; Alberdi, X.; Aramburu, A.; Astarloa, A.; Barandiaran, I.; Basterretxea, K.; Dávila, A.; Erramuzpe, A.; Gabilondo, I.; Lerma-Usabiaga, G.; Monsalve, L.; Mori, L.; Navaridas, J.; Pascual, J., A.; Piriz, J.; Rodrigues, S.; Seijo, O.; Soraluze, A.; Soria, E.; Torres, I.; Uriarte, N.; and Valerdi, J., L.\n\n\n \n\n\n\n In 2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), pages 426-431, 2023. \n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {SiliconBurmuin: A Horizon Europe propelled Neurocomputing Initiative in the Basque Country},\n type = {inproceedings},\n year = {2023},\n keywords = {Industries;Neuroscience;Neuromorphics;Europe;Transforms;Propulsion;Software engineering;Neuromorphic;neurocomputing;neuroscience;computer vision;event-based vision},\n pages = {426-431},\n id = {9e36aac1-7c2d-3ed2-a7d8-cc610ea50126},\n created = {2024-01-26T11:09:27.474Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2024-01-26T11:09:27.474Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {INPROCEEDINGS},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Iturbe, Xabier and Alberdi, Xabier and Aramburu, Ander and Astarloa, Armando and Barandiaran, Iñigo and Basterretxea, Koldo and Dávila, Angélica and Erramuzpe, Asier and Gabilondo, Iñigo and Lerma-Usabiaga, Garikoitz and Monsalve, Lisandro and Mori, Libe and Navaridas, Javier and Pascual, Jose A and Piriz, Joaquin and Rodrigues, Serafim and Seijo, Oscar and Soraluze, Ander and Soria, Edgar and Torres, Ignacio and Uriarte, Nerea and Valerdi, Juan Luis},\n doi = {10.1109/SEAA60479.2023.00070},\n booktitle = {2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)}\n}
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\n \n\n \n \n \n \n \n QVal: a novel routing algorithm for Dragonfly networks.\n \n \n \n\n\n \n Navaridas, J.; and Pascual, J., A.\n\n\n \n\n\n\n In Proceedings of the International Conference on High Performance Computing and Communications, of HPCC'23, 2023. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {QVal: a novel routing algorithm for Dragonfly networks},\n type = {inproceedings},\n year = {2023},\n series = {HPCC'23},\n id = {3de9af61-8e75-32de-ba09-2ce6b566bee0},\n created = {2024-01-26T12:51:53.694Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2024-01-26T12:51:53.694Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Navaridas, Javier and Pascual, Jose A},\n booktitle = {Proceedings of the International Conference on High Performance Computing and Communications}\n}
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\n \n\n \n \n \n \n \n \n Characterizing Permutation-Based Combinatorial Optimization Problems in Fourier Space.\n \n \n \n \n\n\n \n Elorza, A.; Hernando, L.; and Lozano, J., A.\n\n\n \n\n\n\n Evolutionary Computation, 31(3): 163-199. 2 2023.\n \n\n\n\n
\n\n\n\n \n \n \"CharacterizingWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Characterizing Permutation-Based Combinatorial Optimization Problems in Fourier Space},\n type = {article},\n year = {2023},\n pages = {163-199},\n volume = {31},\n websites = {https://doi.org/10.1162/evco_a_00315},\n month = {2},\n id = {34cc5efb-69fb-3792-bca4-9e02e0024ca5},\n created = {2024-02-16T08:39:39.123Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2024-02-16T08:39:39.123Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {Comparing combinatorial optimization problems is a difficult task. They are defined using different criteria and terms: weights, flows, distances, etc. In spite of this apparent discrepancy, on many occasions, they tend to produce problem instances with similar properties. One avenue to compare different problems is to project them onto the same space, in order to have homogeneous representations. Expressing the problems in a unified framework could also lead to the discovery of theoretical properties or the design of new algorithms. This article proposes the use of the Fourier transform over the symmetric group as the tool to project different permutation-based combinatorial optimization problems onto the same space. Based on a previous study (Kondor, 2010), which characterized the Fourier coefficients of the quadratic assignment problem, we describe the Fourier coefficients of three other well-known problems: the symmetric and nonsymmetric traveling salesperson problem and the linear ordering problem. This transformation allows us to gain a better understanding of the intersection between the problems, as well as to bound their intrinsic dimension.},\n bibtype = {article},\n author = {Elorza, Anne and Hernando, Leticia and Lozano, Jose A},\n doi = {10.1162/evco_a_00315},\n journal = {Evolutionary Computation},\n number = {3}\n}
\n
\n\n\n
\n Comparing combinatorial optimization problems is a difficult task. They are defined using different criteria and terms: weights, flows, distances, etc. In spite of this apparent discrepancy, on many occasions, they tend to produce problem instances with similar properties. One avenue to compare different problems is to project them onto the same space, in order to have homogeneous representations. Expressing the problems in a unified framework could also lead to the discovery of theoretical properties or the design of new algorithms. This article proposes the use of the Fourier transform over the symmetric group as the tool to project different permutation-based combinatorial optimization problems onto the same space. Based on a previous study (Kondor, 2010), which characterized the Fourier coefficients of the quadratic assignment problem, we describe the Fourier coefficients of three other well-known problems: the symmetric and nonsymmetric traveling salesperson problem and the linear ordering problem. This transformation allows us to gain a better understanding of the intersection between the problems, as well as to bound their intrinsic dimension.\n
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\n  \n 2022\n \n \n (28)\n \n \n
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\n \n\n \n \n \n \n \n \n Analysis of dominant classes in universal adversarial perturbations.\n \n \n \n \n\n\n \n Vadillo, J.; Santana, R.; and Lozano, J., A.\n\n\n \n\n\n\n Knowledge-Based Systems, 236: 107719. 2022.\n \n\n\n\n
\n\n\n\n \n \n \"AnalysisWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Analysis of dominant classes in universal adversarial perturbations},\n type = {article},\n year = {2022},\n keywords = {Adversarial examples,Deep Neural Networks,Robust speech classification,Universal adversarial perturbations},\n pages = {107719},\n volume = {236},\n websites = {https://www.sciencedirect.com/science/article/pii/S0950705121009643},\n id = {70eea50c-a135-3457-836e-7a12659dd4ad},\n created = {2021-12-12T13:37:48.869Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-12-12T13:37:48.869Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {The reasons why Deep Neural Networks are susceptible to being fooled by adversarial examples remains an open discussion. Indeed, many different strategies can be employed to efficiently generate adversarial attacks, some of them relying on different theoretical justifications. Among these strategies, universal (input-agnostic) perturbations are of particular interest, due to their capability to fool a network independently of the input in which the perturbation is applied. In this work, we investigate an intriguing phenomenon of universal perturbations, which has been reported previously in the literature, yet without a proven justification: universal perturbations change the predicted classes for most inputs into one particular (dominant) class, even if this behavior is not specified during the creation of the perturbation. In order to justify the cause of this phenomenon, we propose a number of hypotheses and experimentally test them using a speech command classification problem in the audio domain as a testbed. Our analyses reveal interesting properties of universal perturbations, suggest new methods to generate such attacks and provide an explanation of dominant classes, under both a geometric and a data-feature perspective.},\n bibtype = {article},\n author = {Vadillo, Jon and Santana, Roberto and Lozano, Jose A},\n doi = {https://doi.org/10.1016/j.knosys.2021.107719},\n journal = {Knowledge-Based Systems}\n}
\n
\n\n\n
\n The reasons why Deep Neural Networks are susceptible to being fooled by adversarial examples remains an open discussion. Indeed, many different strategies can be employed to efficiently generate adversarial attacks, some of them relying on different theoretical justifications. Among these strategies, universal (input-agnostic) perturbations are of particular interest, due to their capability to fool a network independently of the input in which the perturbation is applied. In this work, we investigate an intriguing phenomenon of universal perturbations, which has been reported previously in the literature, yet without a proven justification: universal perturbations change the predicted classes for most inputs into one particular (dominant) class, even if this behavior is not specified during the creation of the perturbation. In order to justify the cause of this phenomenon, we propose a number of hypotheses and experimentally test them using a speech command classification problem in the audio domain as a testbed. Our analyses reveal interesting properties of universal perturbations, suggest new methods to generate such attacks and provide an explanation of dominant classes, under both a geometric and a data-feature perspective.\n
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\n \n\n \n \n \n \n \n \n Informative neural representations of unseen contents during higher-order processing in human brains and deep artificial networks.\n \n \n \n \n\n\n \n Mei, N.; Santana, R.; and Soto, D.\n\n\n \n\n\n\n Nature Human Behaviour. 2022.\n \n\n\n\n
\n\n\n\n \n \n \"InformativeWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Informative neural representations of unseen contents during higher-order processing in human brains and deep artificial networks},\n type = {article},\n year = {2022},\n websites = {https://doi.org/10.1038/s41562-021-01274-7},\n id = {98de5b14-6f16-35a9-8813-fb6f1ec07f15},\n created = {2022-02-04T09:26:33.143Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2022-02-04T09:26:33.143Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {JOUR},\n private_publication = {false},\n abstract = {A framework to pinpoint the scope of unconscious processing is critical to improve models of visual consciousness. Previous research observed brain signatures of unconscious processing in visual cortex, but these were not reliably identified. Further, whether unconscious contents are represented in high-level stages of the ventral visual stream and linked parieto-frontal areas remains unknown. Using a within-subject, high-precision functional magnetic resonance imaging approach, we show that unconscious contents can be decoded from multi-voxel patterns that are highly distributed alongside the ventral visual pathway and also involving parieto-frontal substrates. Classifiers trained with multi-voxel patterns of conscious items generalized to predict the unconscious counterparts, indicating that their neural representations overlap. These findings suggest revisions to models of consciousness such as the neuronal global workspace. We then provide a computational simulation of visual processing/representation without perceptual sensitivity by using deep neural networks performing a similar visual task. The work provides a framework for pinpointing the representation of unconscious knowledge across different task domains.},\n bibtype = {article},\n author = {Mei, Ning and Santana, Roberto and Soto, David},\n doi = {10.1038/s41562-021-01274-7},\n journal = {Nature Human Behaviour}\n}
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\n A framework to pinpoint the scope of unconscious processing is critical to improve models of visual consciousness. Previous research observed brain signatures of unconscious processing in visual cortex, but these were not reliably identified. Further, whether unconscious contents are represented in high-level stages of the ventral visual stream and linked parieto-frontal areas remains unknown. Using a within-subject, high-precision functional magnetic resonance imaging approach, we show that unconscious contents can be decoded from multi-voxel patterns that are highly distributed alongside the ventral visual pathway and also involving parieto-frontal substrates. Classifiers trained with multi-voxel patterns of conscious items generalized to predict the unconscious counterparts, indicating that their neural representations overlap. These findings suggest revisions to models of consciousness such as the neuronal global workspace. We then provide a computational simulation of visual processing/representation without perceptual sensitivity by using deep neural networks performing a similar visual task. The work provides a framework for pinpointing the representation of unconscious knowledge across different task domains.\n
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\n \n\n \n \n \n \n \n Triku: a feature selection method based on nearest neighbors for single-cell data.\n \n \n \n\n\n \n Ascensión AM; Ibáñez-Solé O; Inza I; Izeta A; and Araúzo-Bravo MJ\n\n\n \n\n\n\n GigaDB. 2022.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Triku: a feature selection method based on nearest neighbors for single-cell data},\n type = {article},\n year = {2022},\n id = {654ab12d-82f2-3af4-aa36-08eaa3c3ad75},\n created = {2022-02-04T09:31:57.375Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2022-02-04T09:47:25.931Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n bibtype = {article},\n author = {Ascensión AM, undefined and Ibáñez-Solé O, undefined and Inza I, undefined and Izeta A, undefined and Araúzo-Bravo MJ, undefined},\n journal = {GigaDB}\n}
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\n \n\n \n \n \n \n \n Solving the multi-objective Hamiltonian cycle problem using a Branch-and-Fix based algorithm.\n \n \n \n\n\n \n M. Murua; D. Galar; and R. Santana\n\n\n \n\n\n\n Journal of Computational Science, 60: 101578. 2022.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Solving the multi-objective Hamiltonian cycle problem using a Branch-and-Fix based algorithm},\n type = {article},\n year = {2022},\n pages = {101578},\n volume = {60},\n id = {1c54c214-3fd5-3e30-b905-883b3198cba3},\n created = {2022-02-10T08:42:40.089Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2022-02-10T08:43:09.340Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {M. Murua, undefined and D. Galar, undefined and R. Santana, undefined},\n journal = {Journal of Computational Science}\n}
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\n \n\n \n \n \n \n \n EDA++: Estimation of Distribution Algorithms with Feasibility Conserving Mechanisms for Constrained Continuous Optimization.\n \n \n \n\n\n \n Shirazi, A.; Ceberio, J.; and Lozano, J., A.\n\n\n \n\n\n\n IEEE Transactions on Evolutionary Computation,1. 2022.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {EDA++: Estimation of Distribution Algorithms with Feasibility Conserving Mechanisms for Constrained Continuous Optimization},\n type = {article},\n year = {2022},\n pages = {1},\n id = {99334bda-a5a6-3cca-ac63-a42467c50e33},\n created = {2022-03-02T08:07:30.528Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2022-03-02T08:07:30.528Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {ARTICLE},\n private_publication = {false},\n bibtype = {article},\n author = {Shirazi, Abolfazl and Ceberio, Josu and Lozano, Jose A},\n doi = {10.1109/TEVC.2022.3153933},\n journal = {IEEE Transactions on Evolutionary Computation}\n}
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\n \n\n \n \n \n \n \n \n Time Series Classifier Recommendation by a Meta-Learning Approach.\n \n \n \n \n\n\n \n Abanda, A.; Mori, U.; and Lozano, J., A.\n\n\n \n\n\n\n Pattern Recognition,108671. 2022.\n \n\n\n\n
\n\n\n\n \n \n \"TimeWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Time Series Classifier Recommendation by a Meta-Learning Approach},\n type = {article},\n year = {2022},\n keywords = {Hierarchical inference,Landmarkers,Meta-learning,Meta-targets,Time series classification},\n pages = {108671},\n websites = {https://www.sciencedirect.com/science/article/pii/S0031320322001522},\n id = {bfcf6fc1-2365-3d9a-a547-fa643dfed1f6},\n created = {2022-04-04T08:26:35.762Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2022-04-04T08:26:35.762Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {This work addresses time series classifier recommendation for the first time in the literature by considering several recommendation forms or meta-targets: classifier accuracies, complete ranking, top-M ranking, best set and best classifier. For this, an ad-hoc set of quick estimators of the accuracies of the candidate classifiers (landmarkers) are designed, which are used as predictors for the recommendation system. The performance of our recommender is compared with the performance of a standard method for non-sequential data and a set of baseline methods, which our method outperforms in 7 of the 9 considered scenarios. Since some meta-targets can be inferred from the predictions of other more fine-grained meta-targets, the last part of the work addresses the hierarchical inference of meta-targets. The experimentation suggests that, in many cases, a single model is sufficient to output many types of meta-targets with competitive results.},\n bibtype = {article},\n author = {Abanda, A and Mori, U and Lozano, Jose A},\n doi = {https://doi.org/10.1016/j.patcog.2022.108671},\n journal = {Pattern Recognition}\n}
\n
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\n This work addresses time series classifier recommendation for the first time in the literature by considering several recommendation forms or meta-targets: classifier accuracies, complete ranking, top-M ranking, best set and best classifier. For this, an ad-hoc set of quick estimators of the accuracies of the candidate classifiers (landmarkers) are designed, which are used as predictors for the recommendation system. The performance of our recommender is compared with the performance of a standard method for non-sequential data and a set of baseline methods, which our method outperforms in 7 of the 9 considered scenarios. Since some meta-targets can be inferred from the predictions of other more fine-grained meta-targets, the last part of the work addresses the hierarchical inference of meta-targets. The experimentation suggests that, in many cases, a single model is sufficient to output many types of meta-targets with competitive results.\n
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\n \n\n \n \n \n \n \n \n Benchmarking Object Detection Deep Learning Models in Embedded Devices.\n \n \n \n \n\n\n \n Cantero, D.; Esnaola-Gonzalez, I.; Miguel-Alonso, J.; and Jauregi, E.\n\n\n \n\n\n\n Sensors, 22(11). 2022.\n \n\n\n\n
\n\n\n\n \n \n \"BenchmarkingWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Benchmarking Object Detection Deep Learning Models in Embedded Devices},\n type = {article},\n year = {2022},\n volume = {22},\n websites = {https://www.mdpi.com/1424-8220/22/11/4205},\n id = {ddc7e581-7321-3c86-808a-5d709539355e},\n created = {2022-06-09T09:42:05.903Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2022-06-09T09:42:05.903Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {Article},\n private_publication = {false},\n abstract = {Object detection is an essential capability for performing complex tasks in robotic applications. Today, deep learning (DL) approaches are the basis of state-of-the-art solutions in computer vision, where they provide very high accuracy albeit with high computational costs. Due to the physical limitations of robotic platforms, embedded devices are not as powerful as desktop computers, and adjustments have to be made to deep learning models before transferring them to robotic applications. This work benchmarks deep learning object detection models in embedded devices. Furthermore, some hardware selection guidelines are included, together with a description of the most relevant features of the two boards selected for this benchmark. Embedded electronic devices integrate a powerful AI co-processor to accelerate DL applications. To take advantage of these co-processors, models must be converted to a specific embedded runtime format. Five quantization levels applied to a collection of DL models are considered; two of them allow the execution of models in the embedded general-purpose CPU and are used as the baseline to assess the improvements obtained when running the same models with the three remaining quantization levels in the AI co-processors. The benchmark procedure is explained in detail, and a comprehensive analysis of the collected data is presented. Finally, the feasibility and challenges of the implementation of embedded object detection applications are discussed.},\n bibtype = {article},\n author = {Cantero, David and Esnaola-Gonzalez, Iker and Miguel-Alonso, Jose and Jauregi, Ekaitz},\n doi = {10.3390/s22114205},\n journal = {Sensors},\n number = {11}\n}
\n
\n\n\n
\n Object detection is an essential capability for performing complex tasks in robotic applications. Today, deep learning (DL) approaches are the basis of state-of-the-art solutions in computer vision, where they provide very high accuracy albeit with high computational costs. Due to the physical limitations of robotic platforms, embedded devices are not as powerful as desktop computers, and adjustments have to be made to deep learning models before transferring them to robotic applications. This work benchmarks deep learning object detection models in embedded devices. Furthermore, some hardware selection guidelines are included, together with a description of the most relevant features of the two boards selected for this benchmark. Embedded electronic devices integrate a powerful AI co-processor to accelerate DL applications. To take advantage of these co-processors, models must be converted to a specific embedded runtime format. Five quantization levels applied to a collection of DL models are considered; two of them allow the execution of models in the embedded general-purpose CPU and are used as the baseline to assess the improvements obtained when running the same models with the three remaining quantization levels in the AI co-processors. The benchmark procedure is explained in detail, and a comprehensive analysis of the collected data is presented. Finally, the feasibility and challenges of the implementation of embedded object detection applications are discussed.\n
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\n \n\n \n \n \n \n \n Benchmarking Ethereum Security Tools Against Major Smart Contract Pitfalls and Errors.\n \n \n \n\n\n \n Anguita, S.; Gomez-Goiri, A.; and Miguel-Alonso, J.\n\n\n \n\n\n\n In Actas VII Jornadas Nacionales de Investigación en Ciberseguridad JNIC 2022, pages 331-334, 2022. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Benchmarking Ethereum Security Tools Against Major Smart Contract Pitfalls and Errors},\n type = {inproceedings},\n year = {2022},\n pages = {331-334},\n id = {4c1cd0c4-f250-3bb7-aadf-e145f50b6a71},\n created = {2022-07-04T16:19:56.684Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2022-07-04T16:19:56.684Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {INPROCEEDINGS},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Anguita, S and Gomez-Goiri, A and Miguel-Alonso, J},\n booktitle = {Actas VII Jornadas Nacionales de Investigación en Ciberseguridad JNIC 2022}\n}
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\n \n\n \n \n \n \n \n Non-parametric discretization for probabilistic labeled data.\n \n \n \n\n\n \n Flores, J., L.; Calvo, B.; and Pérez, A.\n\n\n \n\n\n\n Pattern Recognition Letters, 161: 52-58. 2022.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Non-parametric discretization for probabilistic labeled data},\n type = {article},\n year = {2022},\n pages = {52-58},\n volume = {161},\n id = {5c751eb3-d45f-305e-9095-043bf54f7b28},\n created = {2022-08-01T07:43:03.247Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2022-08-01T07:43:03.247Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Flores, Jose Luis and Calvo, Borja and Pérez, Aritz},\n journal = {Pattern Recognition Letters}\n}
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\n \n\n \n \n \n \n \n \n Implementing the Cumulative Difference Plot in the IOHanalyzer.\n \n \n \n \n\n\n \n Arza, E.; Ceberio, J.; Irurozki, E.; and Pérez, A.\n\n\n \n\n\n\n In Proceedings of the Genetic and Evolutionary Computation Conference Companion, of GECCO '22, pages 11-12, 2022. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"ImplementingWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Implementing the Cumulative Difference Plot in the IOHanalyzer},\n type = {inproceedings},\n year = {2022},\n keywords = {benchmarking,first order stochastic dominance,graphical statistics},\n pages = {11-12},\n websites = {https://doi.org/10.1145/3520304.3534050},\n publisher = {Association for Computing Machinery},\n city = {New York, NY, USA},\n series = {GECCO '22},\n id = {1a3583a6-667d-36eb-b387-29ed6e2f1fb1},\n created = {2023-05-23T15:19:18.867Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2023-05-23T15:19:18.867Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {The IOHanalyzer is a web-based framework that enables an easy visualization and comparison of the quality of stochastic optimization algorithms. IOHanalyzer offers several graphical and statistical tools analyze the results of such algorithms. In this work, we implement the cumulative difference plot in the IOHanalyzer. The cumulative difference plot [1] is a graphical approach that compares two samples through the first-order stochastic dominance. It improves upon other graphical approaches with the ability to distinguish between a small magnitude of difference and high uncertainty.},\n bibtype = {inproceedings},\n author = {Arza, Etor and Ceberio, Josu and Irurozki, Ekhiñe and Pérez, Aritz},\n doi = {10.1145/3520304.3534050},\n booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion}\n}
\n
\n\n\n
\n The IOHanalyzer is a web-based framework that enables an easy visualization and comparison of the quality of stochastic optimization algorithms. IOHanalyzer offers several graphical and statistical tools analyze the results of such algorithms. In this work, we implement the cumulative difference plot in the IOHanalyzer. The cumulative difference plot [1] is a graphical approach that compares two samples through the first-order stochastic dominance. It improves upon other graphical approaches with the ability to distinguish between a small magnitude of difference and high uncertainty.\n
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\n \n\n \n \n \n \n \n \n Comparing Two Samples Through Stochastic Dominance: A Graphical Approach.\n \n \n \n \n\n\n \n Arza, E.; Ceberio, J.; Irurozki, E.; and Pérez, A.\n\n\n \n\n\n\n Journal of Computational and Graphical Statistics, 0(0): 1-16. 2022.\n \n\n\n\n
\n\n\n\n \n \n \"ComparingWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Comparing Two Samples Through Stochastic Dominance: A Graphical Approach},\n type = {article},\n year = {2022},\n pages = {1-16},\n volume = {0},\n websites = {https://doi.org/10.1080/10618600.2022.2084405},\n publisher = {Taylor & Francis},\n id = {5b6cd861-7d6e-31be-a53f-04f197d6556f},\n created = {2023-05-23T15:20:52.005Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2023-05-23T15:20:52.005Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Arza, Etor and Ceberio, Josu and Irurozki, Ekhiñe and Pérez, Aritz},\n doi = {10.1080/10618600.2022.2084405},\n journal = {Journal of Computational and Graphical Statistics},\n number = {0}\n}
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\n \n\n \n \n \n \n \n \n Approaching Epistemic and Aleatoric Uncertainty with Evolutionary Optimization: Examples and Challenges.\n \n \n \n \n\n\n \n Ceberio, J.; Cortés, J.; de Vega, F., F.; Garnica, O.; Hidalgo, J., I.; Velasco, J., M.; and Villanueva, R.\n\n\n \n\n\n\n In Proceedings of the Genetic and Evolutionary Computation Conference Companion, of GECCO '22, pages 1909-1915, 2022. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"ApproachingWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Approaching Epistemic and Aleatoric Uncertainty with Evolutionary Optimization: Examples and Challenges},\n type = {inproceedings},\n year = {2022},\n keywords = {evolutionary computation,models with uncertainty,probabilistic calibration,uncertainty quantification},\n pages = {1909-1915},\n websites = {https://doi.org/10.1145/3520304.3533978},\n publisher = {Association for Computing Machinery},\n city = {New York, NY, USA},\n series = {GECCO '22},\n id = {89a854ad-f98e-3cad-ada6-f414a42188de},\n created = {2023-05-23T15:20:52.276Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2023-05-23T15:20:52.276Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {Uncertainty quantification is an emerging area in theory and applications. There are various approaches for modeling and dealing with uncertainty. However, when modeling real-world problems, the calibration considering uncertainty is a relevant issue on which little has been studied. We think that the applicability of evolutionary computation can be much more significant when dealing with uncertainty due to the capabilities of these kinds of algorithms to find suitable solutions in reasonable time budgets. In this context, evolutionary computation approaches have not been investigated extensively, except for a few papers that use metaheuristics and evolutionary algorithms to calibrate models with uncertainty successfully. This paper aims to motivate researchers to study and propose evolutionary algorithms to calibrate models with uncertainty in real-world problems and investigate new proposals. Uncertainty is present in data, measuring processes, evaluation, and models; hence it offers real challenges for the Evolutionary Computation community to propose more sophisticated algorithms and methods. In this paper, we make a general review of how uncertainty quantification has been treated in mathematical modeling, provide ideas on how to interpret uncertainty in specific problems, highlight its drawbacks and show two case studies in medicine.},\n bibtype = {inproceedings},\n author = {Ceberio, Josu and Cortés, Juan-Carlos and de Vega, Francisco Fernández and Garnica, Oscar and Hidalgo, J Ignacio and Velasco, J Manuel and Villanueva, Rafael-Jacinto},\n doi = {10.1145/3520304.3533978},\n booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion}\n}
\n
\n\n\n
\n Uncertainty quantification is an emerging area in theory and applications. There are various approaches for modeling and dealing with uncertainty. However, when modeling real-world problems, the calibration considering uncertainty is a relevant issue on which little has been studied. We think that the applicability of evolutionary computation can be much more significant when dealing with uncertainty due to the capabilities of these kinds of algorithms to find suitable solutions in reasonable time budgets. In this context, evolutionary computation approaches have not been investigated extensively, except for a few papers that use metaheuristics and evolutionary algorithms to calibrate models with uncertainty successfully. This paper aims to motivate researchers to study and propose evolutionary algorithms to calibrate models with uncertainty in real-world problems and investigate new proposals. Uncertainty is present in data, measuring processes, evaluation, and models; hence it offers real challenges for the Evolutionary Computation community to propose more sophisticated algorithms and methods. In this paper, we make a general review of how uncertainty quantification has been treated in mathematical modeling, provide ideas on how to interpret uncertainty in specific problems, highlight its drawbacks and show two case studies in medicine.\n
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\n \n\n \n \n \n \n \n \n Evolutionary Approach to Model Calibration with Uncertainty: An Application to Breast Cancer Growth Model.\n \n \n \n \n\n\n \n Andreu-Vilarroig, C.; Ceberio, J.; Cortés, J.; de Vega, F., F.; Hidalgo, J.; and Villanueva, R.\n\n\n \n\n\n\n In Proceedings of the Genetic and Evolutionary Computation Conference Companion, of GECCO '22, pages 1895-1901, 2022. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"EvolutionaryWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Evolutionary Approach to Model Calibration with Uncertainty: An Application to Breast Cancer Growth Model},\n type = {inproceedings},\n year = {2022},\n keywords = {breast cancer growth,multi-objective particle swarm optimization (MOPSO),probabilistic calibration,random models,uncertainty quantification},\n pages = {1895-1901},\n websites = {https://doi.org/10.1145/3520304.3533948},\n publisher = {Association for Computing Machinery},\n city = {New York, NY, USA},\n series = {GECCO '22},\n id = {4649cc9b-6a9c-3f95-8783-2d875a93c426},\n created = {2023-05-23T15:20:52.541Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2023-05-23T15:20:52.541Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {Dynamical systems in most scientific areas can be modelled using ordinary differential equations or difference equations. However, when analysing and simulating real-world phenomena, model and data uncertainty arises, mainly due to the exclusion from the model of minor aspects of the phenomenon or experimental measurement error in the data. Therefore, more and more models take uncertainty and its quantification into account.One type of such models are random models, in which their parameters are assumed to be random variables. However, this approach to capturing the uncertainty of phenomena has so far been little employed in real-world problems, both because of its inherent difficulty and the lack of data to support the correct choice of model parameter distributions. This problem can be addressed by designing probabilistic calibration algorithms capable of finding distributions of model parameters that explain/capture data uncertainty.In the latter sense, here we propose a technique based on the multi-objective particle swarm optimisation (MOPSO) algorithm to find the parameter probability distributions of a random model that capture the uncertainty of the data. To illustrate the method in a real application, a complete analysis is performed on a simple first-order difference model for breast cancer tumour growth.},\n bibtype = {inproceedings},\n author = {Andreu-Vilarroig, Carlos and Ceberio, Josu and Cortés, Juan-Carlos and de Vega, Francisco Fernández and Hidalgo, José-Ignacio and Villanueva, Rafael-Jacinto},\n doi = {10.1145/3520304.3533948},\n booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion}\n}
\n
\n\n\n
\n Dynamical systems in most scientific areas can be modelled using ordinary differential equations or difference equations. However, when analysing and simulating real-world phenomena, model and data uncertainty arises, mainly due to the exclusion from the model of minor aspects of the phenomenon or experimental measurement error in the data. Therefore, more and more models take uncertainty and its quantification into account.One type of such models are random models, in which their parameters are assumed to be random variables. However, this approach to capturing the uncertainty of phenomena has so far been little employed in real-world problems, both because of its inherent difficulty and the lack of data to support the correct choice of model parameter distributions. This problem can be addressed by designing probabilistic calibration algorithms capable of finding distributions of model parameters that explain/capture data uncertainty.In the latter sense, here we propose a technique based on the multi-objective particle swarm optimisation (MOPSO) algorithm to find the parameter probability distributions of a random model that capture the uncertainty of the data. To illustrate the method in a real application, a complete analysis is performed on a simple first-order difference model for breast cancer tumour growth.\n
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\n \n\n \n \n \n \n \n \n A roadmap for solving optimization problems with estimation of distribution algorithms.\n \n \n \n \n\n\n \n Ceberio, J.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n Natural Computing. 2022.\n \n\n\n\n
\n\n\n\n \n \n \"AWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {A roadmap for solving optimization problems with estimation of distribution algorithms},\n type = {article},\n year = {2022},\n websites = {https://doi.org/10.1007/s11047-022-09913-2},\n id = {c3410a98-cdb3-3c2c-af47-3046bc1b712f},\n created = {2023-05-23T15:20:52.812Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2023-05-23T15:20:52.812Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {In recent decades, Estimation of Distribution Algorithms (EDAs) have gained much popularity in the evolutionary computation community for solving optimization problems. Characterized by the use of probabilistic models to represent the solutions and the interactions between the variables of the problem, EDAs can be applied to either discrete, continuous or mixed domain problems. Due to this robustness, these algorithms have been used to solve a diverse set of real-world and academic optimization problems. However, a straightforward application is only limited to a few cases, and for the general case, an efficient application requires intuition from the problem as well as notable understanding in probabilistic modeling. In this paper, we provide a roadmap for solving optimization problems via EDAs. It is not the aim of the paper to provide a thorough review of EDAs, but to present a guide for those practitioners interested in using the potential of EDAs when solving optimization problems. In order to present a roadmap which is as useful as possible, we address the key aspects involved in the design and application of EDAs, in a sequence of stages: (1) the choice of the codification, (2) the choice of the probability model, (3) strategies to incorporate knowledge about the problem to the model, and (4) balancing the diversification-intensification behavior of the EDA. At each stage, first, the contents are presented together with common practices and advice to follow. Then, an illustration is given with an example which shows different alternatives. In addition to the roadmap, the paper presents current open challenges when developing EDAs, and revises paths for future research advances in the context of EDAs.},\n bibtype = {article},\n author = {Ceberio, Josu and Mendiburu, Alexander and Lozano, Jose A},\n doi = {10.1007/s11047-022-09913-2},\n journal = {Natural Computing}\n}
\n
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\n In recent decades, Estimation of Distribution Algorithms (EDAs) have gained much popularity in the evolutionary computation community for solving optimization problems. Characterized by the use of probabilistic models to represent the solutions and the interactions between the variables of the problem, EDAs can be applied to either discrete, continuous or mixed domain problems. Due to this robustness, these algorithms have been used to solve a diverse set of real-world and academic optimization problems. However, a straightforward application is only limited to a few cases, and for the general case, an efficient application requires intuition from the problem as well as notable understanding in probabilistic modeling. In this paper, we provide a roadmap for solving optimization problems via EDAs. It is not the aim of the paper to provide a thorough review of EDAs, but to present a guide for those practitioners interested in using the potential of EDAs when solving optimization problems. In order to present a roadmap which is as useful as possible, we address the key aspects involved in the design and application of EDAs, in a sequence of stages: (1) the choice of the codification, (2) the choice of the probability model, (3) strategies to incorporate knowledge about the problem to the model, and (4) balancing the diversification-intensification behavior of the EDA. At each stage, first, the contents are presented together with common practices and advice to follow. Then, an illustration is given with an example which shows different alternatives. In addition to the roadmap, the paper presents current open challenges when developing EDAs, and revises paths for future research advances in the context of EDAs.\n
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\n \n\n \n \n \n \n \n \n Analysing Fitness Landscape Rotation For Combinatorial Optimisation.\n \n \n \n \n\n\n \n Alza, J.; Bartlett, M.; Ceberio, J.; and McCall, J.\n\n\n \n\n\n\n In Parallel Problem Solving from Nature – PPSN XVII: 17th International Conference, PPSN 2022, Dortmund, Germany, September 10–14, 2022, Proceedings, Part I, pages 533-547, 2022. Springer-Verlag\n \n\n\n\n
\n\n\n\n \n \n \"AnalysingWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Analysing Fitness Landscape Rotation For Combinatorial Optimisation},\n type = {inproceedings},\n year = {2022},\n keywords = {Combinatorial optimisation,Group theory,Landscape rotation},\n pages = {533-547},\n websites = {https://doi.org/10.1007/978-3-031-14714-2_37},\n publisher = {Springer-Verlag},\n city = {Berlin, Heidelberg},\n id = {e083e6e0-ff37-3358-9c41-0b5b1bab8532},\n created = {2023-05-23T15:20:53.087Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2023-05-23T15:20:53.087Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {Fitness landscape rotation has been widely used in the field of dynamic combinatorial optimisation to generate test problems with academic purposes. This method changes the mapping between solutions and objective values, but preserves the structure of the fitness landscape. In this work, the rotation of the landscape in the combinatorial domain is theoretically analysed using concepts of discrete mathematics. Certainly, the preservation of the neighbourhood relationship between the solutions and the structure of the landscape are studied in detail. Based on the theoretical insights obtained, landscape rotation has been employed as a strategy to escape from local optima when local search algorithms get stuck. Conducted experiments confirm the good performance of the rotation-based local search algorithms to perturb the search towards unexplored local optima on a set of instances of the linear ordering problem.},\n bibtype = {inproceedings},\n author = {Alza, Joan and Bartlett, Mark and Ceberio, Josu and McCall, John},\n doi = {10.1007/978-3-031-14714-2_37},\n booktitle = {Parallel Problem Solving from Nature – PPSN XVII: 17th International Conference, PPSN 2022, Dortmund, Germany, September 10–14, 2022, Proceedings, Part I}\n}
\n
\n\n\n
\n Fitness landscape rotation has been widely used in the field of dynamic combinatorial optimisation to generate test problems with academic purposes. This method changes the mapping between solutions and objective values, but preserves the structure of the fitness landscape. In this work, the rotation of the landscape in the combinatorial domain is theoretically analysed using concepts of discrete mathematics. Certainly, the preservation of the neighbourhood relationship between the solutions and the structure of the landscape are studied in detail. Based on the theoretical insights obtained, landscape rotation has been employed as a strategy to escape from local optima when local search algorithms get stuck. Conducted experiments confirm the good performance of the rotation-based local search algorithms to perturb the search towards unexplored local optima on a set of instances of the linear ordering problem.\n
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\n \n\n \n \n \n \n \n Bayesian Performance Analysis for Algorithm Ranking Comparison.\n \n \n \n\n\n \n Rojas-Delgado, J.; Ceberio, J.; Calvo, B.; and Lozano, J., A.\n\n\n \n\n\n\n IEEE Transactions on Evolutionary Computation, 26(6): 1281-1292. 2022.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Bayesian Performance Analysis for Algorithm Ranking Comparison},\n type = {article},\n year = {2022},\n pages = {1281-1292},\n volume = {26},\n id = {0e7eb3ff-8431-3f82-a0cf-6ca866d9d622},\n created = {2023-05-23T15:20:53.495Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2023-05-23T15:20:53.495Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Rojas-Delgado, Jairo and Ceberio, Josu and Calvo, Borja and Lozano, Jose A},\n doi = {10.1109/TEVC.2022.3208110},\n journal = {IEEE Transactions on Evolutionary Computation},\n number = {6}\n}
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\n \n\n \n \n \n \n \n \n Ad-hoc explanation for time series classification.\n \n \n \n \n\n\n \n Abanda, A.; Mori, U.; and Lozano, J., A.\n\n\n \n\n\n\n Knowledge-Based Systems, 252: 109366. 2022.\n \n\n\n\n
\n\n\n\n \n \n \"Ad-hocWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Ad-hoc explanation for time series classification},\n type = {article},\n year = {2022},\n keywords = {Ad-hoc explanation,Agnostic,Robustness,Time series classification,Time series transformations},\n pages = {109366},\n volume = {252},\n websites = {https://www.sciencedirect.com/science/article/pii/S0950705122006852},\n id = {f0754a73-c198-370e-b4cb-6b9e7737793c},\n created = {2023-05-23T15:20:54.498Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2023-05-23T15:20:54.498Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {In this work, a perturbation-based model-agnostic explanation method for time series classification is presented. One of the main novelties of the proposed method is that the considered perturbations are interpretable and specific for time series. In real-world time series, variations in the speed or the scale of a particular action, for instance, may determine the class, so modifying this type of characteristic leads to ad-hoc explanations for time series. To this end, four perturbations or transformations are proposed: warp, scale, noise, and slice. Given a transformation, an interval of a series is considered relevant for the prediction of a classifier if a transformation in this interval changes the prediction. Another novelty is that the method provides a two-level explanation: a high-level explanation, where the robustness of the prediction with respect to a particular transformation is measured, and a low-level explanation, where the relevance of each region of the time series in the prediction is visualized. In order to analyze and validate our proposal, first some illustrative examples are provided, and then a thorough quantitative evaluation is carried out using a specifically designed evaluation procedure.},\n bibtype = {article},\n author = {Abanda, A and Mori, U and Lozano, J A},\n doi = {https://doi.org/10.1016/j.knosys.2022.109366},\n journal = {Knowledge-Based Systems}\n}
\n
\n\n\n
\n In this work, a perturbation-based model-agnostic explanation method for time series classification is presented. One of the main novelties of the proposed method is that the considered perturbations are interpretable and specific for time series. In real-world time series, variations in the speed or the scale of a particular action, for instance, may determine the class, so modifying this type of characteristic leads to ad-hoc explanations for time series. To this end, four perturbations or transformations are proposed: warp, scale, noise, and slice. Given a transformation, an interval of a series is considered relevant for the prediction of a classifier if a transformation in this interval changes the prediction. Another novelty is that the method provides a two-level explanation: a high-level explanation, where the robustness of the prediction with respect to a particular transformation is measured, and a low-level explanation, where the relevance of each region of the time series in the prediction is visualized. In order to analyze and validate our proposal, first some illustrative examples are provided, and then a thorough quantitative evaluation is carried out using a specifically designed evaluation procedure.\n
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\n \n\n \n \n \n \n \n Adversarial Perturbations for Evolutionary Optimization.\n \n \n \n\n\n \n Garciarena, U.; Vadillo, J.; Mendiburu, A.; and Santana, R.\n\n\n \n\n\n\n In Nicosia, G.; Ojha, V.; La Malfa, E.; La Malfa, G.; Jansen, G.; Pardalos, P., M.; Giuffrida, G.; and Umeton, R., editor(s), Machine Learning, Optimization, and Data Science, pages 408-422, 2022. Springer International Publishing\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Adversarial Perturbations for Evolutionary Optimization},\n type = {inproceedings},\n year = {2022},\n pages = {408-422},\n publisher = {Springer International Publishing},\n city = {Cham},\n id = {3a49fe31-b234-3d7f-9a6e-352430101c07},\n created = {2023-05-23T15:20:54.763Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2023-05-23T15:20:54.763Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {Sampling methods are a critical step for model-based evolutionary algorithms, their goal being the generation of new and promising individuals based on the information provided by the model. Adversarial perturbations have been proposed as a way to create samples that deceive neural networks. In this paper we introduce the idea of creating adversarial perturbations that correspond to promising solutions of the search space. A surrogate neural network is ``fooled'' by an adversarial perturbation algorithm until it produces solutions that are likely to be of higher fitness than the present ones. Using a benchmark of functions with varying levels of difficulty, we investigate the performance of a number of adversarial perturbation techniques as sampling methods. The paper also proposes a technique to enhance the effect that adversarial perturbations produce in the network. While adversarial perturbations on their own are not able to produce evolutionary algorithms that compete with state of the art methods, they provide a novel and promising way to combine local optimizers with evolutionary algorithms.},\n bibtype = {inproceedings},\n author = {Garciarena, Unai and Vadillo, Jon and Mendiburu, Alexander and Santana, Roberto},\n editor = {Nicosia, Giuseppe and Ojha, Varun and La Malfa, Emanuele and La Malfa, Gabriele and Jansen, Giorgio and Pardalos, Panos M and Giuffrida, Giovanni and Umeton, Renato},\n booktitle = {Machine Learning, Optimization, and Data Science}\n}
\n
\n\n\n
\n Sampling methods are a critical step for model-based evolutionary algorithms, their goal being the generation of new and promising individuals based on the information provided by the model. Adversarial perturbations have been proposed as a way to create samples that deceive neural networks. In this paper we introduce the idea of creating adversarial perturbations that correspond to promising solutions of the search space. A surrogate neural network is ``fooled'' by an adversarial perturbation algorithm until it produces solutions that are likely to be of higher fitness than the present ones. Using a benchmark of functions with varying levels of difficulty, we investigate the performance of a number of adversarial perturbation techniques as sampling methods. The paper also proposes a technique to enhance the effect that adversarial perturbations produce in the network. While adversarial perturbations on their own are not able to produce evolutionary algorithms that compete with state of the art methods, they provide a novel and promising way to combine local optimizers with evolutionary algorithms.\n
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\n \n\n \n \n \n \n \n A Multivariate Time Series Streaming Classifier for Predicting Hard Drive Failures [Application Notes].\n \n \n \n\n\n \n Ircio, J.; Lojo, A.; Mori, U.; and Lozano, J., A.\n\n\n \n\n\n\n IEEE Computational Intelligence Magazine, 17(1): 102-114. 2022.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {A Multivariate Time Series Streaming Classifier for Predicting Hard Drive Failures [Application Notes]},\n type = {article},\n year = {2022},\n pages = {102-114},\n volume = {17},\n id = {4d8195c4-3c3f-30f3-ac1b-78a2b51858d4},\n created = {2023-05-23T15:20:55.054Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2023-05-23T15:20:55.054Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Ircio, Josu and Lojo, Aizea and Mori, Usue and Lozano, Jose A},\n doi = {10.1109/MCI.2021.3129962},\n journal = {IEEE Computational Intelligence Magazine},\n number = {1}\n}
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\n \n\n \n \n \n \n \n A mathematical analysis of EDAs with distance-based exponential models.\n \n \n \n\n\n \n Unanue, I.; Merino, M.; and Lozano, J., A.\n\n\n \n\n\n\n Memetic Computing, 14(3): 305-334. 2022.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {A mathematical analysis of EDAs with distance-based exponential models},\n type = {article},\n year = {2022},\n pages = {305-334},\n volume = {14},\n publisher = {Springer},\n id = {a68e0447-6d86-3954-836e-27bf96fde878},\n created = {2023-05-23T15:20:55.320Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2023-05-23T15:20:55.320Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Unanue, Imanol and Merino, Mar\\'\\ia and Lozano, Jose A},\n journal = {Memetic Computing},\n number = {3}\n}
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\n \n\n \n \n \n \n \n Are the statistical tests the best way to deal with the biomarker selection problem?.\n \n \n \n\n\n \n Urkullu, A.; Perez, A.; and Calvo, B.\n\n\n \n\n\n\n Knowledge and Information Systems, 64: 1549-1570. 2022.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Are the statistical tests the best way to deal with the biomarker selection problem?},\n type = {article},\n year = {2022},\n pages = {1549-1570},\n volume = {64},\n id = {f2e8d906-d66e-3085-92bd-218b90ddbb02},\n created = {2023-05-23T15:20:55.600Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2023-05-23T15:20:55.600Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Urkullu, Ari and Perez, Aritz and Calvo, Borja},\n journal = {Knowledge and Information Systems}\n}
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\n \n\n \n \n \n \n \n An Efficient Split-Merge Re-Start for the KK-Means Algorithm.\n \n \n \n\n\n \n Capó, M.; Pérez, A.; and Lozano, J., A.\n\n\n \n\n\n\n IEEE Transactions on Knowledge and Data Engineering, 34(4): 1618-1627. 2022.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {An Efficient Split-Merge Re-Start for the KK-Means Algorithm},\n type = {article},\n year = {2022},\n pages = {1618-1627},\n volume = {34},\n id = {92705ef3-2a78-3527-8b61-ee8c62dd92b9},\n created = {2023-05-23T15:20:55.883Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2023-05-23T15:20:55.883Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Capó, Marco and Pérez, Aritz and Lozano, Jose A},\n doi = {10.1109/TKDE.2020.3002926},\n journal = {IEEE Transactions on Knowledge and Data Engineering},\n number = {4}\n}
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\n \n\n \n \n \n \n \n \n An active adaptation strategy for streaming time series classification based on elastic similarity measures.\n \n \n \n \n\n\n \n Oregi, I.; Pérez, A.; Del Ser, J.; and Lozano, J., A.\n\n\n \n\n\n\n Neural Computing and Applications, 34(16): 13237-13252. 2022.\n \n\n\n\n
\n\n\n\n \n \n \"AnWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {An active adaptation strategy for streaming time series classification based on elastic similarity measures},\n type = {article},\n year = {2022},\n pages = {13237-13252},\n volume = {34},\n websites = {https://doi.org/10.1007/s00521-022-07358-3},\n id = {f9d65544-9b7b-3d36-8c3f-756dbea9f8f9},\n created = {2023-05-23T15:20:56.152Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2023-05-23T15:20:56.152Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {In streaming time series classification problems, the goal is to predict the label associated to the most recently received observations over the stream according to a set of categorized reference patterns. In on-line scenarios, data arise from non-stationary processes, which results in a succession of different patterns or events. This work presents an active adaptation strategy that allows time series classifiers to accommodate to the dynamics of streamed time series data. Specifically, our approach consists of a classifier that detects changes between events over streaming time series. For this purpose, the classifier uses features of the dynamic time warping measure computed between the streamed data and a set of reference patterns. When classifying a streaming series, the proposed pattern end detector analyzes such features to predict changes and adapt off-line time series classifiers to newly arriving events. To evaluate the performance of the proposed scheme, we employ the pattern end detection model along with dynamic time warping-based nearest neighbor classifiers over a benchmark of ten time series classification problems. The obtained results present exciting insights into the detection accuracy and latency performance of the proposed strategy.},\n bibtype = {article},\n author = {Oregi, Izaskun and Pérez, Aritz and Del Ser, Javier and Lozano, Jose A},\n doi = {10.1007/s00521-022-07358-3},\n journal = {Neural Computing and Applications},\n number = {16}\n}
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\n In streaming time series classification problems, the goal is to predict the label associated to the most recently received observations over the stream according to a set of categorized reference patterns. In on-line scenarios, data arise from non-stationary processes, which results in a succession of different patterns or events. This work presents an active adaptation strategy that allows time series classifiers to accommodate to the dynamics of streamed time series data. Specifically, our approach consists of a classifier that detects changes between events over streaming time series. For this purpose, the classifier uses features of the dynamic time warping measure computed between the streamed data and a set of reference patterns. When classifying a streaming series, the proposed pattern end detector analyzes such features to predict changes and adapt off-line time series classifiers to newly arriving events. To evaluate the performance of the proposed scheme, we employ the pattern end detection model along with dynamic time warping-based nearest neighbor classifiers over a benchmark of ten time series classification problems. The obtained results present exciting insights into the detection accuracy and latency performance of the proposed strategy.\n
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\n \n\n \n \n \n \n \n \n LASSO for Streaming Data with Adaptative Filtering.\n \n \n \n \n\n\n \n Capó, M.; Pérez, A.; and Lozano, J., A.\n\n\n \n\n\n\n Statistics and Computing, 33(1). 11 2022.\n \n\n\n\n
\n\n\n\n \n \n \"LASSOWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {LASSO for Streaming Data with Adaptative Filtering},\n type = {article},\n year = {2022},\n keywords = {Adaptative filtering,Homotopy,LASSO,Streaming data},\n volume = {33},\n websites = {https://doi.org/10.1007/s11222-022-10181-4},\n month = {11},\n publisher = {Kluwer Academic Publishers},\n city = {USA},\n id = {7a105ac7-9e5a-31cf-9a65-3ce37360db9b},\n created = {2023-05-23T15:20:56.463Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2023-05-23T15:20:56.463Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {Streaming data is ubiquitous in modern machine learning, and so the development of scalable algorithms to analyze this sort of information is a topic of current interest. On the other hand, the problem of l1-penalized least-square regression, commonly referred to as LASSO, is a quite popular data mining technique, which is commonly used for feature selection. In this work, we develop a homotopy-based solver for LASSO, on a streaming data context, that massively speeds up its convergence by extracting the most information out of the solution prior receiving the latest batch of data. Since these batches may show a non-stationary behavior, our solver also includes an adaptive filter that improves the predictability of our method in this scenario. Besides different theoretical properties, we additionally compare empirically our solver to the state-of-the-art: LARS, coordinate descent and Garrigues and Ghaoui's data streaming homotopy. The obtained results show our approach to massively reduce the computational time require to convergence for the previous approaches, reducing up to 3, 4 and 5 orders of magnitude of running time with respect to LARS, coordinate descent and Garrigues and Ghaoui's homotopy, respectively.},\n bibtype = {article},\n author = {Capó, Marco and Pérez, Aritz and Lozano, José A},\n doi = {10.1007/s11222-022-10181-4},\n journal = {Statistics and Computing},\n number = {1}\n}
\n
\n\n\n
\n Streaming data is ubiquitous in modern machine learning, and so the development of scalable algorithms to analyze this sort of information is a topic of current interest. On the other hand, the problem of l1-penalized least-square regression, commonly referred to as LASSO, is a quite popular data mining technique, which is commonly used for feature selection. In this work, we develop a homotopy-based solver for LASSO, on a streaming data context, that massively speeds up its convergence by extracting the most information out of the solution prior receiving the latest batch of data. Since these batches may show a non-stationary behavior, our solver also includes an adaptive filter that improves the predictability of our method in this scenario. Besides different theoretical properties, we additionally compare empirically our solver to the state-of-the-art: LARS, coordinate descent and Garrigues and Ghaoui's data streaming homotopy. The obtained results show our approach to massively reduce the computational time require to convergence for the previous approaches, reducing up to 3, 4 and 5 orders of magnitude of running time with respect to LARS, coordinate descent and Garrigues and Ghaoui's homotopy, respectively.\n
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\n \n\n \n \n \n \n \n \n A Grammar-Based GP Approach Applied to the Design of Deep Neural Networks.\n \n \n \n \n\n\n \n Lima, R., H., R.; Magalhães, D.; Pozo, A.; Mendiburu, A.; and Santana, R.\n\n\n \n\n\n\n Genetic Programming and Evolvable Machines, 23(3): 427-452. 9 2022.\n \n\n\n\n
\n\n\n\n \n \n \"AWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {A Grammar-Based GP Approach Applied to the Design of Deep Neural Networks},\n type = {article},\n year = {2022},\n keywords = {Automatic design,Deep neural networks,Evolutionary algorithms,Genetic programming,Grammatical evolution},\n pages = {427-452},\n volume = {23},\n websites = {https://doi.org/10.1007/s10710-022-09432-0},\n month = {9},\n publisher = {Kluwer Academic Publishers},\n city = {USA},\n id = {51bf1a5a-0f4f-3e4a-be43-49628ab7e9f1},\n created = {2023-05-23T15:20:59.330Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2023-05-23T15:20:59.330Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {Deep Learning has been very successful in automating the feature engineering process, widely applied for various tasks, such as speech recognition, classification, segmentation of images, time-series forecasting, among others. Deep neural networks (DNNs) incorporate the power to learn patterns through data, following an end-to-end fashion and expand the applicability in real world problems, since less pre-processing is necessary. With the fast growth in both scale and complexity, a new challenge has emerged regarding the design and configuration of DNNs. In this work, we present a study on applying an evolutionary grammar-based genetic programming algorithm (GP) as a unified approach to the design of DNNs. Evolutionary approaches have been growing in popularity for this subject as Neuroevolution is studied more. We validate our approach in three different applications: the design of Convolutional Neural Networks for image classification, Graph Neural Networks for text classification, and U-Nets for image segmentation. The results show that evolutionary grammar-based GP can efficiently generate different DNN architectures, adapted to each problem, employing choices that differ from what is usually seen in networks designed by hand. This approach has shown a lot of promise regarding the design of architectures, reaching competitive results with their counterparts.},\n bibtype = {article},\n author = {Lima, Ricardo H R and Magalhães, Dimmy and Pozo, Aurora and Mendiburu, Alexander and Santana, Roberto},\n doi = {10.1007/s10710-022-09432-0},\n journal = {Genetic Programming and Evolvable Machines},\n number = {3}\n}
\n
\n\n\n
\n Deep Learning has been very successful in automating the feature engineering process, widely applied for various tasks, such as speech recognition, classification, segmentation of images, time-series forecasting, among others. Deep neural networks (DNNs) incorporate the power to learn patterns through data, following an end-to-end fashion and expand the applicability in real world problems, since less pre-processing is necessary. With the fast growth in both scale and complexity, a new challenge has emerged regarding the design and configuration of DNNs. In this work, we present a study on applying an evolutionary grammar-based genetic programming algorithm (GP) as a unified approach to the design of DNNs. Evolutionary approaches have been growing in popularity for this subject as Neuroevolution is studied more. We validate our approach in three different applications: the design of Convolutional Neural Networks for image classification, Graph Neural Networks for text classification, and U-Nets for image segmentation. The results show that evolutionary grammar-based GP can efficiently generate different DNN architectures, adapted to each problem, employing choices that differ from what is usually seen in networks designed by hand. This approach has shown a lot of promise regarding the design of architectures, reaching competitive results with their counterparts.\n
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\n \n\n \n \n \n \n \n An embedding space for SARS-CoV-2 epitope-based vaccines.\n \n \n \n\n\n \n Santana, R.\n\n\n \n\n\n\n In EUROPEAN JOURNAL OF CLINICAL INVESTIGATION, volume 52, 2022. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {An embedding space for SARS-CoV-2 epitope-based vaccines},\n type = {inproceedings},\n year = {2022},\n volume = {52},\n id = {0cca4173-6ea2-316d-9762-50f17141f49c},\n created = {2023-05-23T15:20:59.615Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2023-05-23T15:20:59.615Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Santana, R},\n booktitle = {EUROPEAN JOURNAL OF CLINICAL INVESTIGATION}\n}
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\n \n\n \n \n \n \n \n \n Triku: a feature selection method based on nearest neighbors for single-cell data.\n \n \n \n \n\n\n \n M Ascensión, A.; Ibáñez-Solé, O.; Inza, I.; Izeta, A.; and Araúzo-Bravo, M., J.\n\n\n \n\n\n\n GigaScience, 11: giac017. 1 2022.\n \n\n\n\n
\n\n\n\n \n \n \"Triku:Website\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Triku: a feature selection method based on nearest neighbors for single-cell data},\n type = {article},\n year = {2022},\n pages = {giac017},\n volume = {11},\n websites = {https://doi.org/10.1093/gigascience/giac017},\n month = {1},\n id = {a7a1ea0f-ead5-3da3-9e7d-5314e2e8c64b},\n created = {2024-01-09T10:46:48.636Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2024-01-09T10:46:48.636Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {Feature selection is a relevant step in the analysis of single-cell RNA sequencing datasets. Most of the current feature selection methods are based on general univariate descriptors of the data such as the dispersion or the percentage of zeros. Despite the use of correction methods, the generality of these feature selection methods biases the genes selected towards highly expressed genes, instead of the genes defining the cell populations of the dataset.Triku is a feature selection method that favors genes defining the main cell populations. It does so by selecting genes expressed by groups of cells that are close in the k-nearest neighbor graph. The expression of these genes is higher than the expected expression if the k-cells were chosen at random. Triku efficiently recovers cell populations present in artificial and biological benchmarking datasets, based on adjusted Rand index, normalized mutual information, supervised classification, and silhouette coefficient measurements. Additionally, gene sets selected by triku are more likely to be related to relevant Gene Ontology terms and contain fewer ribosomal and mitochondrial genes.Triku is developed in Python 3 and is available at https://github.com/alexmascension/triku.},\n bibtype = {article},\n author = {M Ascensión, Alex and Ibáñez-Solé, Olga and Inza, Iñaki and Izeta, Ander and Araúzo-Bravo, Marcos J},\n doi = {10.1093/gigascience/giac017},\n journal = {GigaScience}\n}
\n
\n\n\n
\n Feature selection is a relevant step in the analysis of single-cell RNA sequencing datasets. Most of the current feature selection methods are based on general univariate descriptors of the data such as the dispersion or the percentage of zeros. Despite the use of correction methods, the generality of these feature selection methods biases the genes selected towards highly expressed genes, instead of the genes defining the cell populations of the dataset.Triku is a feature selection method that favors genes defining the main cell populations. It does so by selecting genes expressed by groups of cells that are close in the k-nearest neighbor graph. The expression of these genes is higher than the expected expression if the k-cells were chosen at random. Triku efficiently recovers cell populations present in artificial and biological benchmarking datasets, based on adjusted Rand index, normalized mutual information, supervised classification, and silhouette coefficient measurements. Additionally, gene sets selected by triku are more likely to be related to relevant Gene Ontology terms and contain fewer ribosomal and mitochondrial genes.Triku is developed in Python 3 and is available at https://github.com/alexmascension/triku.\n
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\n \n\n \n \n \n \n \n Transitions from P to NP-hardness: the case of the Linear Ordering Problem.\n \n \n \n\n\n \n Elorza, A.; Hernando, L.; and Lozano, J., A.\n\n\n \n\n\n\n In 2022 IEEE Congress on Evolutionary Computation (CEC), pages 1-8, 2022. \n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Transitions from P to NP-hardness: the case of the Linear Ordering Problem},\n type = {inproceedings},\n year = {2022},\n keywords = {Fourier transforms;NP-hard problem;Evolutionary computation;Linear programming;combinatorial optimization;permutations;linear ordering problem;NP-hardness;complexity transitions},\n pages = {1-8},\n id = {4986f23a-dd41-3899-ae16-977f8005109a},\n created = {2024-02-16T08:40:50.387Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2024-02-16T08:40:50.387Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {INPROCEEDINGS},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Elorza, Anne and Hernando, Leticia and Lozano, Jose A},\n doi = {10.1109/CEC55065.2022.9870392},\n booktitle = {2022 IEEE Congress on Evolutionary Computation (CEC)}\n}
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\n  \n 2021\n \n \n (33)\n \n \n
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\n \n\n \n \n \n \n \n Analysis of Bayesian Network Learning Techniques for a Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm: a case study on MNK Landscape.\n \n \n \n\n\n \n Martins, M., S., R.; Yafrani, M., E.; Delgado, M.; Lüders, R.; Santana, R.; Siqueira, H., V.; Akcay, H., G.; and Ahiod, B.\n\n\n \n\n\n\n Journal of Heuristics. 2021.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Analysis of Bayesian Network Learning Techniques for a Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm: a case study on MNK Landscape},\n type = {article},\n year = {2021},\n id = {56295e1c-bf1a-3748-893d-84322f4e98f2},\n created = {2021-11-12T08:30:23.157Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:23.157Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {This work investigates different Bayesian network structure learning techniques by thoroughly studying several variants of Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm (HMOBEDA), applied to the MNK Landscape combinatorial problem. In the experiments, we evaluate the performance considering three different aspects: optimization abilities, robustness and learning efficiency. Results for instances of multi- and many-objective MNK-landscape show that, score-based structure learning algorithms appear to be the best choice. In particular, HMOBEDAk2 was capable of producing results comparable with the other variants in terms of the runtime of convergence and the coverage of the final Pareto front, with the additional advantage of providing solutions that are less sensible to noise while the variability of the corresponding Bayesian network models is reduced.},\n bibtype = {article},\n author = {Martins, Marcella S R and Yafrani, Mohamed El and Delgado, Myriam and Lüders, Ricardo and Santana, Roberto and Siqueira, Hugo V and Akcay, Huseyin G and Ahiod, Belaïd},\n journal = {Journal of Heuristics}\n}
\n
\n\n\n
\n This work investigates different Bayesian network structure learning techniques by thoroughly studying several variants of Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm (HMOBEDA), applied to the MNK Landscape combinatorial problem. In the experiments, we evaluate the performance considering three different aspects: optimization abilities, robustness and learning efficiency. Results for instances of multi- and many-objective MNK-landscape show that, score-based structure learning algorithms appear to be the best choice. In particular, HMOBEDAk2 was capable of producing results comparable with the other variants in terms of the runtime of convergence and the coverage of the final Pareto front, with the additional advantage of providing solutions that are less sensible to noise while the variability of the corresponding Bayesian network models is reduced.\n
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\n \n\n \n \n \n \n \n Analysis of the sensitivity of the End-Of-Turn Detection task to errors generated by the Automatic Speech Recognition process.\n \n \n \n\n\n \n Montenegro, C.; Santana, R.; and Lozano, J., A.\n\n\n \n\n\n\n Engineering Applications of Artificial Intelligence, 100: 104189. 2021.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Analysis of the sensitivity of the End-Of-Turn Detection task to errors generated by the Automatic Speech Recognition process},\n type = {article},\n year = {2021},\n keywords = {Automatic speech recognition,End of turn detection,Natural language processing,Neural networks,Spoken dialogue systems},\n pages = {104189},\n volume = {100},\n id = {a23fa6e1-a3a5-31aa-bea3-f509c214dd02},\n created = {2021-11-12T08:30:31.200Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:31.200Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {An End-Of-Turn Detection Module (EOTD-M) is an essential component of automatic Spoken Dialogue Systems. The capability of correctly detecting whether a user's utterance has ended or not improves the accuracy in interpreting the meaning of the message and decreases the latency in the answer. Usually, in dialogue systems, an EOTD-M is coupled with an Automatic Speech Recognition Module (ASR-M) to transmit complete utterances to the Natural Language Understanding unit. Mistakes in the ASR-M transcription can have a strong effect on the performance of the EOTD-M. The actual extent of this effect depends on the particular combination of ASR-M transcription errors and the sentence featurization techniques implemented as part of the EOTD-M. In this paper we investigate this important relationship for an EOTD-M based on semantic information and particular characteristics of the speakers (speech profiles). We introduce an Automatic Speech Recognition Simulator (ASR-SIM) that models different types of semantic mistakes in the ASR-M transcription as well as different speech profiles. We use the simulator to evaluate the sensitivity to ASR-M mistakes of a Long Short-Term Memory network classifier trained in EOTD with different featurization techniques. Our experiments reveal the different ways in which the performance of the model is influenced by the ASR-M errors. We corroborate that not only is the ASR-SIM useful to estimate the performance of an EOTD-M in customized noisy scenarios, but it can also be used to generate training datasets with the expected error rates of real working conditions, which leads to better performance.},\n bibtype = {article},\n author = {Montenegro, César and Santana, Roberto and Lozano, Jose A},\n journal = {Engineering Applications of Artificial Intelligence}\n}
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\n\n\n
\n An End-Of-Turn Detection Module (EOTD-M) is an essential component of automatic Spoken Dialogue Systems. The capability of correctly detecting whether a user's utterance has ended or not improves the accuracy in interpreting the meaning of the message and decreases the latency in the answer. Usually, in dialogue systems, an EOTD-M is coupled with an Automatic Speech Recognition Module (ASR-M) to transmit complete utterances to the Natural Language Understanding unit. Mistakes in the ASR-M transcription can have a strong effect on the performance of the EOTD-M. The actual extent of this effect depends on the particular combination of ASR-M transcription errors and the sentence featurization techniques implemented as part of the EOTD-M. In this paper we investigate this important relationship for an EOTD-M based on semantic information and particular characteristics of the speakers (speech profiles). We introduce an Automatic Speech Recognition Simulator (ASR-SIM) that models different types of semantic mistakes in the ASR-M transcription as well as different speech profiles. We use the simulator to evaluate the sensitivity to ASR-M mistakes of a Long Short-Term Memory network classifier trained in EOTD with different featurization techniques. Our experiments reveal the different ways in which the performance of the model is influenced by the ASR-M errors. We corroborate that not only is the ASR-SIM useful to estimate the performance of an EOTD-M in customized noisy scenarios, but it can also be used to generate training datasets with the expected error rates of real working conditions, which leads to better performance.\n
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\n \n\n \n \n \n \n \n Towards Automatic Construction of Multi-Network Models for Heterogeneous Multi-Task Learning.\n \n \n \n\n\n \n Garciarena, U.; Mendiburu, A.; and Santana, R.\n\n\n \n\n\n\n ACM Trans. Knowl. Discov. Data, 15(2). 5 2021.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Towards Automatic Construction of Multi-Network Models for Heterogeneous Multi-Task Learning},\n type = {article},\n year = {2021},\n keywords = {Multi-task learning,deep neural networks,neural architecture search},\n volume = {15},\n month = {5},\n publisher = {Association for Computing Machinery},\n id = {e4d74b8b-a92c-35aa-a8ed-8676ec6e2865},\n created = {2021-11-12T08:31:03.538Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:03.538Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {Multi-task learning, as it is understood nowadays, consists of using one single model to carry out several similar tasks. From classifying hand-written characters of different alphabets to figuring out how to play several Atari games using reinforcement learning, multi-task models have been able to widen their performance range across different tasks, although these tasks are usually of a similar nature. In this work, we attempt to expand this range even further, by including heterogeneous tasks in a single learning procedure. To do so, we firstly formally define a multi-network model, identifying the necessary components and characteristics to allow different adaptations of said model depending on the tasks it is required to fulfill. Secondly, employing the formal definition as a starting point, we develop an illustrative model example consisting of three different tasks (classification, regression, and data sampling). The performance of this illustrative model is then analyzed, showing its capabilities. Motivated by the results of the analysis, we enumerate a set of open challenges and future research lines over which the full potential of the proposed model definition can be exploited.},\n bibtype = {article},\n author = {Garciarena, Unai and Mendiburu, Alexander and Santana, Roberto},\n journal = {ACM Trans. Knowl. Discov. Data},\n number = {2}\n}
\n
\n\n\n
\n Multi-task learning, as it is understood nowadays, consists of using one single model to carry out several similar tasks. From classifying hand-written characters of different alphabets to figuring out how to play several Atari games using reinforcement learning, multi-task models have been able to widen their performance range across different tasks, although these tasks are usually of a similar nature. In this work, we attempt to expand this range even further, by including heterogeneous tasks in a single learning procedure. To do so, we firstly formally define a multi-network model, identifying the necessary components and characteristics to allow different adaptations of said model depending on the tasks it is required to fulfill. Secondly, employing the formal definition as a starting point, we develop an illustrative model example consisting of three different tasks (classification, regression, and data sampling). The performance of this illustrative model is then analyzed, showing its capabilities. Motivated by the results of the analysis, we enumerate a set of open challenges and future research lines over which the full potential of the proposed model definition can be exploited.\n
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\n \n\n \n \n \n \n \n \n Evolution of Gaussian Process kernels for machine translation post-editing effort estimation.\n \n \n \n \n\n\n \n Roman, I.; Santana, R.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n Annals of Mathematics and Artificial Intelligence. 2021.\n \n\n\n\n
\n\n\n\n \n \n \"EvolutionWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{\n title = {Evolution of Gaussian Process kernels for machine translation post-editing effort estimation},\n type = {article},\n year = {2021},\n websites = {https://doi.org/10.1007/s10472-021-09751-5},\n id = {f33f00f8-ea8d-31d6-bd68-61f26ec42ef6},\n created = {2021-11-12T08:31:52.175Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:52.175Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {In many Natural Language Processing problems the combination of machine learning and optimization techniques is essential. One of these problems is the estimation of the human effort needed to improve a text that has been translated using a machine translation method. Recent advances in this area have shown that Gaussian Processes can be effective in post-editing effort prediction. However, Gaussian Processes require a kernel function to be defined, the choice of which highly influences the quality of the prediction. On the other hand, the extraction of features from the text can be very labor-intensive, although recent advances in sentence embedding have shown that this process can be automated. In this paper, we use a Genetic Programming algorithm to evolve kernels for Gaussian Processes to predict post-editing effort based on sentence embeddings. We show that the combination of evolutionary optimization and Gaussian Processes removes the need for a-priori specification of the kernel choice, and, by using a multi-objective variant of the Genetic Programming approach, kernels that are suitable for predicting several metrics can be learned. We also investigate the effect that the choice of the sentence embedding method has on the kernel learning process.},\n bibtype = {article},\n author = {Roman, Ibai and Santana, Roberto and Mendiburu, Alexander and Lozano, Jose A},\n doi = {10.1007/s10472-021-09751-5},\n journal = {Annals of Mathematics and Artificial Intelligence}\n}
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\n In many Natural Language Processing problems the combination of machine learning and optimization techniques is essential. One of these problems is the estimation of the human effort needed to improve a text that has been translated using a machine translation method. Recent advances in this area have shown that Gaussian Processes can be effective in post-editing effort prediction. However, Gaussian Processes require a kernel function to be defined, the choice of which highly influences the quality of the prediction. On the other hand, the extraction of features from the text can be very labor-intensive, although recent advances in sentence embedding have shown that this process can be automated. In this paper, we use a Genetic Programming algorithm to evolve kernels for Gaussian Processes to predict post-editing effort based on sentence embeddings. We show that the combination of evolutionary optimization and Gaussian Processes removes the need for a-priori specification of the kernel choice, and, by using a multi-objective variant of the Genetic Programming approach, kernels that are suitable for predicting several metrics can be learned. We also investigate the effect that the choice of the sentence embedding method has on the kernel learning process.\n
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\n \n\n \n \n \n \n \n Analyzing Elementary Landscapes in Graph Partitioning Problems.\n \n \n \n\n\n \n Dorado, I.; and Ceberio, J.\n\n\n \n\n\n\n In XIV Congreso Español de Metaheuristicas, Algoritmos Evolutivos y Bioinspirados (MAEB 2020-21), pages 459-464, 2021. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Analyzing Elementary Landscapes in Graph Partitioning Problems},\n type = {inproceedings},\n year = {2021},\n pages = {459-464},\n city = {Malaga},\n id = {a6cb6fdf-d843-3d10-a490-933054f676aa},\n created = {2021-11-12T08:32:03.718Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:03.718Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Dorado, I and Ceberio, Josu},\n booktitle = {XIV Congreso Español de Metaheuristicas, Algoritmos Evolutivos y Bioinspirados (MAEB 2020-21)}\n}
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\n \n\n \n \n \n \n \n \n On the Symmetry of the Quadratic Assignment Problem through Elementary Landscape Decomposition.\n \n \n \n \n\n\n \n Benavides, X.; Ceberio, J.; and Hernando, L.\n\n\n \n\n\n\n In Proceedings of the Genetic and Evolutionary Computation Conference Companion, pages 1414-1422, 2021. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"OnWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {On the Symmetry of the Quadratic Assignment Problem through Elementary Landscape Decomposition},\n type = {inproceedings},\n year = {2021},\n keywords = {elementary landscapes,quadratic assignment problem,symmetry},\n pages = {1414-1422},\n websites = {https://doi.org/10.1145/3449726.3463191},\n publisher = {Association for Computing Machinery},\n city = {New York, NY, USA},\n id = {ff5f2139-4417-3220-9fa3-c10f6ce50694},\n created = {2021-11-12T08:32:03.987Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:03.987Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {When designing meta-heuristic strategies to optimize the quadratic assignment problem (QAP), it is important to take into account the specific characteristics of the instance to be solved. One of the characteristics that has been pointed out as having the potential to affect the performance of optimization algorithms is the symmetry of the distance and flow matrices that form the QAP.In this paper, we further investigate the impact of the symmetry of the QAP on the performance of meta-heuristic algorithms, focusing on local search based methods. The analysis is carried out using the elementary landscape decomposition (ELD) of the problem under the swap neighborhood. First, we study the number of local optima and the relative contribution of the elementary components on a benchmark composed of different types of instances. Secondly, we propose a specific local search algorithm based on the ELD in order to experimentally validate the effects of the symmetry. The analysis carried out shows that the symmetry of the QAP is a relevant feature that influences both the characteristics of the elementary components and the performance of local search based algorithms.},\n bibtype = {inproceedings},\n author = {Benavides, Xabier and Ceberio, Josu and Hernando, Leticia},\n doi = {10.1145/3449726.3463191},\n booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion}\n}
\n
\n\n\n
\n When designing meta-heuristic strategies to optimize the quadratic assignment problem (QAP), it is important to take into account the specific characteristics of the instance to be solved. One of the characteristics that has been pointed out as having the potential to affect the performance of optimization algorithms is the symmetry of the distance and flow matrices that form the QAP.In this paper, we further investigate the impact of the symmetry of the QAP on the performance of meta-heuristic algorithms, focusing on local search based methods. The analysis is carried out using the elementary landscape decomposition (ELD) of the problem under the swap neighborhood. First, we study the number of local optima and the relative contribution of the elementary components on a benchmark composed of different types of instances. Secondly, we propose a specific local search algorithm based on the ELD in order to experimentally validate the effects of the symmetry. The analysis carried out shows that the symmetry of the QAP is a relevant feature that influences both the characteristics of the elementary components and the performance of local search based algorithms.\n
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\n \n\n \n \n \n \n \n Universal Adversarial Examples in Speech Command Classification.\n \n \n \n\n\n \n Vadillo, J.; and Santana, R.\n\n\n \n\n\n\n In XIX Conference of the Spanish Association for Artificial Intelligence (CAEPIA), pages 642-647, 2021. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Universal Adversarial Examples in Speech Command Classification},\n type = {inproceedings},\n year = {2021},\n pages = {642-647},\n city = {Malaga},\n id = {e7c7d03a-6baa-3697-99e7-47234b0ae2b6},\n created = {2021-11-12T08:32:04.818Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:04.818Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Vadillo, Jon and Santana, Roberto},\n booktitle = {XIX Conference of the Spanish Association for Artificial Intelligence (CAEPIA)}\n}
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\n \n\n \n \n \n \n \n A two stage stochastic optimization model for ambulance location-allocation under coverage equity and response time efficiency.\n \n \n \n\n\n \n Gago, I.; Aldasoro, U.; Ceberio, J.; and Merino, M.\n\n\n \n\n\n\n In The 34th Conference of the European Chapter on Combinatorial Optimization (ECCO-2021), 2021. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {A two stage stochastic optimization model for ambulance location-allocation under coverage equity and response time efficiency},\n type = {inproceedings},\n year = {2021},\n id = {ac28772a-7e94-3615-a0cc-a29307b7e6d4},\n created = {2021-11-12T08:32:05.089Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:05.089Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Gago, Imanol and Aldasoro, Unai and Ceberio, Josu and Merino, Maria},\n booktitle = {The 34th Conference of the European Chapter on Combinatorial Optimization (ECCO-2021)}\n}
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\n \n\n \n \n \n \n \n Bayesian Optimization for Permutations Spaces: A preliminary approach.\n \n \n \n\n\n \n Jimenez, D.; Ceberio, J.; and Lozano, J., A.\n\n\n \n\n\n\n In XIV Congreso Español de Metaheuristicas, Algoritmos Evolutivos y Bioinspirados (MAEB 2020-21), pages 453-458, 2021. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Bayesian Optimization for Permutations Spaces: A preliminary approach},\n type = {inproceedings},\n year = {2021},\n pages = {453-458},\n city = {Malaga},\n id = {63dd1ca1-d3c9-3534-8a1e-2ee3f33a1494},\n created = {2021-11-12T08:32:06.269Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:06.269Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Jimenez, D and Ceberio, Josu and Lozano, J A},\n booktitle = {XIV Congreso Español de Metaheuristicas, Algoritmos Evolutivos y Bioinspirados (MAEB 2020-21)}\n}
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\n \n\n \n \n \n \n \n A General Framework Based on Walsh Decomposition for Combinatorial Optimization Problems.\n \n \n \n\n\n \n Unanue, I.; Merino, M.; and Lozano, J., A.\n\n\n \n\n\n\n In 2021 IEEE Congress on Evolutionary Computation (CEC), pages 391-398, 2021. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {A General Framework Based on Walsh Decomposition for Combinatorial Optimization Problems},\n type = {inproceedings},\n year = {2021},\n pages = {391-398},\n id = {3f4ab7dd-cf0d-3101-afc7-321fee3e2a8e},\n created = {2021-11-12T08:32:06.516Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:06.516Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Unanue, Imanol and Merino, Mar\\'\\ia and Lozano, Jose A},\n booktitle = {2021 IEEE Congress on Evolutionary Computation (CEC)}\n}
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\n \n\n \n \n \n \n \n \n Towards the Landscape Rotation as a Perturbation Strategy on the Quadratic Assignment Problem.\n \n \n \n \n\n\n \n Alza, J.; Bartlett, M.; Ceberio, J.; and McCall, J.\n\n\n \n\n\n\n In Proceedings of the Genetic and Evolutionary Computation Conference Companion, pages 1405-1413, 2021. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"TowardsWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Towards the Landscape Rotation as a Perturbation Strategy on the Quadratic Assignment Problem},\n type = {inproceedings},\n year = {2021},\n keywords = {landscape rotation,quadratic assignment problem},\n pages = {1405-1413},\n websites = {https://doi.org/10.1145/3449726.3463139},\n publisher = {Association for Computing Machinery},\n city = {New York, NY, USA},\n id = {19406273-3956-3f9a-9644-b27e397885b2},\n created = {2021-11-12T08:32:06.774Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:06.774Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {Recent work in combinatorial optimisation have demonstrated that neighbouring solutions of a local optima may belong to more favourable attraction basins. In this sense, the perturbation strategy plays a critical role on local search based algorithms to kick the search of the algorithm into more prominent areas of the space. In this paper, we investigate the landscape rotation as a perturbation strategy to redirect the search of an stuck algorithm. This technique rearranges the mapping of solutions to different objective values without altering important properties of the problem's landscape such as the number and quality of optima, among others. Particularly, we investigate two rotation based perturbation strategies: (i) a profoundness rotation method and (ii) a broadness rotation method. These methods are applied into the stochastic hill-climbing heuristic and tested and compared on different instances of the quadratic assignment problem against other algorithm versions. Performed experiments reveal that the landscape rotation is an efficient perturbation strategy to shift the search in a controlled way. Nevertheless, an empirical investigation of the landscape rotation demonstrates that it needs to be cautiously manipulated in the permutation space since a small rotation does not necessarily mean a small disturbance in the fitness landscape.},\n bibtype = {inproceedings},\n author = {Alza, Joan and Bartlett, Mark and Ceberio, Josu and McCall, John},\n doi = {10.1145/3449726.3463139},\n booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion}\n}
\n
\n\n\n
\n Recent work in combinatorial optimisation have demonstrated that neighbouring solutions of a local optima may belong to more favourable attraction basins. In this sense, the perturbation strategy plays a critical role on local search based algorithms to kick the search of the algorithm into more prominent areas of the space. In this paper, we investigate the landscape rotation as a perturbation strategy to redirect the search of an stuck algorithm. This technique rearranges the mapping of solutions to different objective values without altering important properties of the problem's landscape such as the number and quality of optima, among others. Particularly, we investigate two rotation based perturbation strategies: (i) a profoundness rotation method and (ii) a broadness rotation method. These methods are applied into the stochastic hill-climbing heuristic and tested and compared on different instances of the quadratic assignment problem against other algorithm versions. Performed experiments reveal that the landscape rotation is an efficient perturbation strategy to shift the search in a controlled way. Nevertheless, an empirical investigation of the landscape rotation demonstrates that it needs to be cautiously manipulated in the permutation space since a small rotation does not necessarily mean a small disturbance in the fitness landscape.\n
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\n \n\n \n \n \n \n \n On the exploitation of neuroevolutionary information.\n \n \n \n\n\n \n Garciarena, U.; Lourenço, N.; Machado, P.; Santana, R.; and Mendiburu, A.\n\n\n \n\n\n\n In Proceedings of the Genetic and Evolutionary Computation Conference Companion, pages 279-280, 2021. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {On the exploitation of neuroevolutionary information},\n type = {inproceedings},\n year = {2021},\n pages = {279-280},\n id = {39032be9-5ed6-39c2-845e-f5259cd65dd3},\n created = {2021-11-12T08:32:07.588Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:07.588Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Garciarena, Unai and Lourenço, Nuno and Machado, Penousal and Santana, Roberto and Mendiburu, Alexander},\n booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion}\n}
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\n \n\n \n \n \n \n \n Semantic Technologies Towards Missing Values Imputation.\n \n \n \n\n\n \n Esnaola-Gonzalez, I.; Garciarena, U.; and Bermúdez, J.\n\n\n \n\n\n\n In International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, pages 191-196, 2021. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Semantic Technologies Towards Missing Values Imputation},\n type = {inproceedings},\n year = {2021},\n pages = {191-196},\n id = {69127615-4d19-3ff0-a36f-8c0efb28aa3b},\n created = {2021-11-12T08:32:07.844Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:07.844Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Esnaola-Gonzalez, Iker and Garciarena, Unai and Bermúdez, Jesús},\n booktitle = {International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems}\n}
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\n \n\n \n \n \n \n \n \n A machine learning approach to predict healthcare cost of breast cancer patients.\n \n \n \n \n\n\n \n Rakshit, P.; Zaballa, O.; Pérez, A.; Gómez-Inhiesto, E.; Acaiturri-Ayesta, M., T.; and Lozano, J., A.\n\n\n \n\n\n\n Scientific Reports, 11(1): 12441. 2021.\n \n\n\n\n
\n\n\n\n \n \n \"AWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {A machine learning approach to predict healthcare cost of breast cancer patients},\n type = {article},\n year = {2021},\n pages = {12441},\n volume = {11},\n websites = {https://doi.org/10.1038/s41598-021-91580-x},\n id = {1da746ec-2a4c-3be2-a823-6e09bb8f2fc2},\n created = {2021-11-12T08:32:09.091Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:09.091Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {This paper presents a novel machine learning approach to perform an early prediction of the healthcare cost of breast cancer patients. The learning phase of our prediction method considers the following two steps: (1) in the first step, the patients are clustered taking into account the sequences of actions undergoing similar clinical activities and ensuring similar healthcare costs, and (2) a Markov chain is then learned for each group to describe the action-sequences of the patients in the cluster. A two step procedure is undertaken in the prediction phase: (1) first, the healthcare cost of a new patient's treatment is estimated based on the average healthcare cost of its k-nearest neighbors in each group, and (2) finally, an aggregate measure of the healthcare cost estimated by each group is used as the final predicted cost.Experiments undertaken reveal a mean absolute percentage error as small as 6%, even when half of the clinical records of a patient is available, substantiating the early prediction capability of the proposed method. Comparative analysis substantiates the superiority of the proposed algorithm over the state-of-the-art techniques.},\n bibtype = {article},\n author = {Rakshit, Pratyusha and Zaballa, Onintze and Pérez, Aritz and Gómez-Inhiesto, Elisa and Acaiturri-Ayesta, Maria T and Lozano, Jose A},\n doi = {10.1038/s41598-021-91580-x},\n journal = {Scientific Reports},\n number = {1}\n}
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\n This paper presents a novel machine learning approach to perform an early prediction of the healthcare cost of breast cancer patients. The learning phase of our prediction method considers the following two steps: (1) in the first step, the patients are clustered taking into account the sequences of actions undergoing similar clinical activities and ensuring similar healthcare costs, and (2) a Markov chain is then learned for each group to describe the action-sequences of the patients in the cluster. A two step procedure is undertaken in the prediction phase: (1) first, the healthcare cost of a new patient's treatment is estimated based on the average healthcare cost of its k-nearest neighbors in each group, and (2) finally, an aggregate measure of the healthcare cost estimated by each group is used as the final predicted cost.Experiments undertaken reveal a mean absolute percentage error as small as 6%, even when half of the clinical records of a patient is available, substantiating the early prediction capability of the proposed method. Comparative analysis substantiates the superiority of the proposed algorithm over the state-of-the-art techniques.\n
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\n \n\n \n \n \n \n \n \n On solving cycle problems with Branch-and-Cut: extending shrinking and exact subcycle elimination separation algorithms.\n \n \n \n \n\n\n \n Kobeaga, G.; Merino, M.; and Lozano, J., A.\n\n\n \n\n\n\n Annals of Operations Research, 305(1): 107-136. 2021.\n \n\n\n\n
\n\n\n\n \n \n \"OnWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {On solving cycle problems with Branch-and-Cut: extending shrinking and exact subcycle elimination separation algorithms},\n type = {article},\n year = {2021},\n pages = {107-136},\n volume = {305},\n websites = {https://doi.org/10.1007/s10479-021-04210-0},\n id = {7a4e4268-dbac-3596-b03b-348bc782beee},\n created = {2021-11-12T08:32:09.361Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:09.361Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {In this paper, we extend techniques developed in the context of the Travelling Salesperson Problem for cycle problems. Particularly, we study the shrinking of support graphs and the exact algorithms for subcycle elimination separation problems. The efficient application of the considered techniques has proved to be essential in the Travelling Salesperson Problem when solving large size problems by Branch-and-Cut, and this has been the motivation behind this work. Regarding the shrinking of support graphs, we prove the validity of the Padberg–Rinaldi general shrinking rules and the Crowder–Padberg subcycle-safe shrinking rules. Concerning the subcycle separation problems, we extend two exact separation algorithms, the Dynamic Hong and the Extended Padberg–Grötschel algorithms, which are shown to be superior to the ones used so far in the literature of cycle problems. The proposed techniques are empirically tested in 24 subcycle elimination problem instances generated by solving the Orienteering Problem (involving up to 15,112 vertices) with Branch-and-Cut. The experiments suggest the relevance of the proposed techniques for cycle problems. The obtained average speedup for the subcycle separation problems in the Orienteering Problem when the proposed techniques are used together is around 50 times in medium-sized instances and around 250 times in large-sized instances.},\n bibtype = {article},\n author = {Kobeaga, Gorka and Merino, Mar\\'\\ia and Lozano, Jose A},\n doi = {10.1007/s10479-021-04210-0},\n journal = {Annals of Operations Research},\n number = {1}\n}
\n
\n\n\n
\n In this paper, we extend techniques developed in the context of the Travelling Salesperson Problem for cycle problems. Particularly, we study the shrinking of support graphs and the exact algorithms for subcycle elimination separation problems. The efficient application of the considered techniques has proved to be essential in the Travelling Salesperson Problem when solving large size problems by Branch-and-Cut, and this has been the motivation behind this work. Regarding the shrinking of support graphs, we prove the validity of the Padberg–Rinaldi general shrinking rules and the Crowder–Padberg subcycle-safe shrinking rules. Concerning the subcycle separation problems, we extend two exact separation algorithms, the Dynamic Hong and the Extended Padberg–Grötschel algorithms, which are shown to be superior to the ones used so far in the literature of cycle problems. The proposed techniques are empirically tested in 24 subcycle elimination problem instances generated by solving the Orienteering Problem (involving up to 15,112 vertices) with Branch-and-Cut. The experiments suggest the relevance of the proposed techniques for cycle problems. The obtained average speedup for the subcycle separation problems in the Orienteering Problem when the proposed techniques are used together is around 50 times in medium-sized instances and around 250 times in large-sized instances.\n
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\n \n\n \n \n \n \n \n Semantic Composition of Word-Embeddings with Genetic Programming.\n \n \n \n\n\n \n Santana, R.\n\n\n \n\n\n\n Heuristics for Optimization and Learning, pages 409-423. Springer, 2021.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inbook{\n type = {inbook},\n year = {2021},\n pages = {409-423},\n publisher = {Springer},\n id = {a6ba81d4-ce78-37d9-9c98-f06cb18f03a3},\n created = {2021-11-12T08:32:09.630Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:09.630Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {incollection},\n private_publication = {false},\n bibtype = {inbook},\n author = {Santana, R},\n chapter = {Semantic Composition of Word-Embeddings with Genetic Programming},\n title = {Heuristics for Optimization and Learning}\n}
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\n \n\n \n \n \n \n \n A Review on outlier/Anomaly Detection in Time Series Data.\n \n \n \n\n\n \n Blázquez-Garc\\'\\ia, A.; Conde, A.; Mori, U.; and Lozano, J., A.\n\n\n \n\n\n\n ACM Computing Surveys (CSUR), 54(3): 1-33. 2021.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {A Review on outlier/Anomaly Detection in Time Series Data},\n type = {article},\n year = {2021},\n pages = {1-33},\n volume = {54},\n publisher = {ACM New York, NY, USA},\n id = {93de8693-0998-33c3-b939-d5f31df89a89},\n created = {2021-11-12T08:32:09.900Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:09.900Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Blázquez-Garc\\'\\ia, Ane and Conde, Angel and Mori, Usue and Lozano, Jose A},\n journal = {ACM Computing Surveys (CSUR)},\n number = {3}\n}
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\n \n\n \n \n \n \n \n Automatic Design of Deep Neural Networks Applied to Image Segmentation Problems.\n \n \n \n\n\n \n Lima, R.; Pozo, A.; Mendiburu, A.; and Santana, R.\n\n\n \n\n\n\n In European Conference on Genetic Programming (Part of EvoStar), pages 98-113, 2021. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Automatic Design of Deep Neural Networks Applied to Image Segmentation Problems},\n type = {inproceedings},\n year = {2021},\n pages = {98-113},\n id = {c89c0067-1b2c-3895-b351-9d8f4654421d},\n created = {2021-11-12T08:32:10.805Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:10.805Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Lima, Ricardo and Pozo, Aurora and Mendiburu, Alexander and Santana, Roberto},\n booktitle = {European Conference on Genetic Programming (Part of EvoStar)}\n}
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\n \n\n \n \n \n \n \n Delineation of site-specific management zones using estimation of distribution algorithms.\n \n \n \n\n\n \n Velasco, J.; Vicencio, S.; Lozano, J., A.; and Cid-Garcia, N., M.\n\n\n \n\n\n\n International Transactions in Operational Research. 2021.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Delineation of site-specific management zones using estimation of distribution algorithms},\n type = {article},\n year = {2021},\n publisher = {Wiley Online Library},\n id = {92cf732e-8dd7-3a3a-a50c-53aca337b98d},\n created = {2021-11-12T08:32:11.177Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:11.177Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Velasco, Jonas and Vicencio, Salvador and Lozano, Jose A and Cid-Garcia, Nestor M},\n journal = {International Transactions in Operational Research}\n}
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\n \n\n \n \n \n \n \n Estimation of distribution algorithms for the computation of innovation estimators of diffusion processes.\n \n \n \n\n\n \n Arenas, Z., G.; Jimenez, J., C.; Lozada-Chang, L.; and Santana, R.\n\n\n \n\n\n\n Mathematics and Computers in Simulation, 187: 449-467. 2021.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Estimation of distribution algorithms for the computation of innovation estimators of diffusion processes},\n type = {article},\n year = {2021},\n pages = {449-467},\n volume = {187},\n publisher = {Elsevier},\n id = {341b294e-96a8-3272-ba65-608e73a8d335},\n created = {2021-11-12T08:32:12.752Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:12.752Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Arenas, Zochil González and Jimenez, Juan Carlos and Lozada-Chang, Li-Vang and Santana, Roberto},\n journal = {Mathematics and Computers in Simulation}\n}
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\n \n\n \n \n \n \n \n Probabilistic Load Forecasting Based on Adaptive Online Learning.\n \n \n \n\n\n \n Alvarez, V.; Mazuelas, S.; and Lozano, J., A.\n\n\n \n\n\n\n IEEE Transactions on Power Systems. 2021.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Probabilistic Load Forecasting Based on Adaptive Online Learning},\n type = {article},\n year = {2021},\n publisher = {IEEE},\n id = {b103be2c-5bce-3a20-b633-d9d5d11cd1ec},\n created = {2021-11-12T08:32:14.795Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:14.795Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Alvarez, Verónica and Mazuelas, Santiago and Lozano, Jose Antonio},\n journal = {IEEE Transactions on Power Systems}\n}
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\n \n\n \n \n \n \n \n \n Evolving Gaussian process kernels from elementary mathematical expressions for time series extrapolation.\n \n \n \n \n\n\n \n Roman, I.; Santana, R.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n Neurocomputing, 462: 426-439. 2021.\n \n\n\n\n
\n\n\n\n \n \n \"EvolvingWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Evolving Gaussian process kernels from elementary mathematical expressions for time series extrapolation},\n type = {article},\n year = {2021},\n keywords = {Evolutionary search,Gaussian processes,Genetic programming,Kernel learning,Time series extrapolation},\n pages = {426-439},\n volume = {462},\n websites = {https://www.sciencedirect.com/science/article/pii/S0925231221012042},\n id = {8a94be1f-8d6d-3b61-a7b6-66fddcb97ecd},\n created = {2021-11-12T08:32:15.342Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:15.342Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {Choosing the best kernel is crucial in many Machine Learning applications. Gaussian Processes are a state-of-the-art technique for regression and classification that heavily relies on a kernel function. However, in the Gaussian Processes literature, kernels have usually been either ad hoc designed, selected from a predefined set, or searched for in a space of compositions of kernels which have been defined a priori. In this paper, we propose a Genetic Programming algorithm that represents a kernel function as a tree of elementary mathematical expressions. By means of this representation, a wider set of kernels can be modeled, where potentially better solutions can be found, although new challenges also arise. The proposed algorithm is able to overcome these difficulties and find kernels that accurately model the characteristics of the data. This method has been tested in several real-world time series extrapolation problems, improving the state-of-the-art results while reducing the complexity of the kernels.},\n bibtype = {article},\n author = {Roman, Ibai and Santana, Roberto and Mendiburu, Alexander and Lozano, Jose A},\n doi = {https://doi.org/10.1016/j.neucom.2021.08.020},\n journal = {Neurocomputing}\n}
\n
\n\n\n
\n Choosing the best kernel is crucial in many Machine Learning applications. Gaussian Processes are a state-of-the-art technique for regression and classification that heavily relies on a kernel function. However, in the Gaussian Processes literature, kernels have usually been either ad hoc designed, selected from a predefined set, or searched for in a space of compositions of kernels which have been defined a priori. In this paper, we propose a Genetic Programming algorithm that represents a kernel function as a tree of elementary mathematical expressions. By means of this representation, a wider set of kernels can be modeled, where potentially better solutions can be found, although new challenges also arise. The proposed algorithm is able to overcome these difficulties and find kernels that accurately model the characteristics of the data. This method has been tested in several real-world time series extrapolation problems, improving the state-of-the-art results while reducing the complexity of the kernels.\n
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\n \n\n \n \n \n \n \n Simulation Framework for Orbit Propagation and Space Trajectory Visualization.\n \n \n \n\n\n \n Shirazi, A.; Ceberio, J.; and Lozano, J., A.\n\n\n \n\n\n\n IEEE Aerospace and Electronic Systems Magazine, 36(8): 4-20. 2021.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Simulation Framework for Orbit Propagation and Space Trajectory Visualization},\n type = {article},\n year = {2021},\n pages = {4-20},\n volume = {36},\n publisher = {IEEE},\n id = {4e3eb4e2-a79f-3478-9ed9-44a5174283c5},\n created = {2021-11-12T08:32:15.664Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:15.664Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Shirazi, Abolfazl and Ceberio, Josu and Lozano, Jose A},\n journal = {IEEE Aerospace and Electronic Systems Magazine},\n number = {8}\n}
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\n \n\n \n \n \n \n \n \n On the Human Evaluation of Universal Audio Adversarial Perturbations.\n \n \n \n \n\n\n \n Vadillo, J.; and Santana, R.\n\n\n \n\n\n\n Computers & Security,102495. 2021.\n \n\n\n\n
\n\n\n\n \n \n \"OnWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {On the Human Evaluation of Universal Audio Adversarial Perturbations},\n type = {article},\n year = {2021},\n keywords = {adversarial examples,deep neural networks,human perception,robust speech recognition,speech command classification},\n pages = {102495},\n websites = {https://www.sciencedirect.com/science/article/pii/S0167404821003199},\n id = {d8222d66-a92c-35da-a73a-8340be2c0861},\n created = {2021-11-12T08:32:16.178Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:16.178Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {Human-machine interaction is increasingly dependent on speech communication, mainly due to the remarkable performance of Machine Learning models in speech recognition tasks. However, these models can be fooled by adversarial examples, which are inputs intentionally perturbed to produce a wrong prediction without the changes being noticeable to humans. While much research has focused on developing new techniques to generate adversarial perturbations, less attention has been given to aspects that determine whether and how the perturbations are noticed by humans. This question is relevant since high fooling rates of proposed adversarial perturbation strategies are only valuable if the perturbations are not detectable. In this paper we investigate to which extent the distortion metrics proposed in the literature for audio adversarial examples, and which are commonly applied to evaluate the effectiveness of methods for generating these attacks, are a reliable measure of the human perception of the perturbations. Using an analytical framework, and an experiment in which 36 subjects evaluate audio adversarial examples according to different factors, we demonstrate that the metrics employed by convention are not a reliable measure of the perceptual similarity of adversarial examples in the audio domain.},\n bibtype = {article},\n author = {Vadillo, Jon and Santana, Roberto},\n doi = {https://doi.org/10.1016/j.cose.2021.102495},\n journal = {Computers & Security}\n}
\n
\n\n\n
\n Human-machine interaction is increasingly dependent on speech communication, mainly due to the remarkable performance of Machine Learning models in speech recognition tasks. However, these models can be fooled by adversarial examples, which are inputs intentionally perturbed to produce a wrong prediction without the changes being noticeable to humans. While much research has focused on developing new techniques to generate adversarial perturbations, less attention has been given to aspects that determine whether and how the perturbations are noticed by humans. This question is relevant since high fooling rates of proposed adversarial perturbation strategies are only valuable if the perturbations are not detectable. In this paper we investigate to which extent the distortion metrics proposed in the literature for audio adversarial examples, and which are commonly applied to evaluate the effectiveness of methods for generating these attacks, are a reliable measure of the human perception of the perturbations. Using an analytical framework, and an experiment in which 36 subjects evaluate audio adversarial examples according to different factors, we demonstrate that the metrics employed by convention are not a reliable measure of the perceptual similarity of adversarial examples in the audio domain.\n
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\n \n\n \n \n \n \n \n \n Exploratory analysis of the Monte Carlo tree search for solving the linear ordering problem.\n \n \n \n \n\n\n \n Garmendia, A., I.; Ceberio, J.; and Mendiburu, A.\n\n\n \n\n\n\n In Proceedings of the Genetic and Evolutionary Computation Conference Companion, pages 1433-1441, 2021. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"ExploratoryWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
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@inproceedings{\n title = {Exploratory analysis of the Monte Carlo tree search for solving the linear ordering problem},\n type = {inproceedings},\n year = {2021},\n keywords = {Monte-Carlo tree search,combinatorial optimization,linear ordering problem},\n pages = {1433-1441},\n websites = {https://doi.org/10.1145/3449726.3463163},\n publisher = {Association for Computing Machinery},\n city = {New York, NY, USA},\n id = {89693fbf-27dc-3eba-bd18-da189b5d8b97},\n created = {2021-11-12T08:32:16.444Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:16.444Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {Monte-Carlo Tree Search has delivered great results in two-player game-playing and its current success has turned it into a popular choice of study in different use cases. Recently, many works have applied MCTS and, especially, its neural variant, as an end-to-end approach to solve Combinatorial Optimization Problems. However, its efficiency for solving regular Combinatorial Problems has still to be studied.In this paper, we investigate the capability of the Monte-Carlo Tree Search algorithm to optimize permutation-based problems, making use of problem-specific knowledge. Particularly, we focus on the well-known Linear Ordering Problem (LOP), taking advantage of the easy computation of the expected and upper bound fitness of partial permutations. Moreover, we introduce a Multi-Objective Optimization approach to deal with the exploration-exploitation dilemma during the tree search. Conducted experiments show that MCTS obtains better results than classical constructive algorithms, though its performance is not obviously comparable to state-of-the-art results. Based on its ability for guiding structured searches, its scalability, convergence and search space coverage, MCTS could open new research trends in the optimization area.},\n bibtype = {inproceedings},\n author = {Garmendia, Andoni I and Ceberio, Josu and Mendiburu, Alexander},\n doi = {10.1145/3449726.3463163},\n booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion}\n}
\n
\n\n\n
\n Monte-Carlo Tree Search has delivered great results in two-player game-playing and its current success has turned it into a popular choice of study in different use cases. Recently, many works have applied MCTS and, especially, its neural variant, as an end-to-end approach to solve Combinatorial Optimization Problems. However, its efficiency for solving regular Combinatorial Problems has still to be studied.In this paper, we investigate the capability of the Monte-Carlo Tree Search algorithm to optimize permutation-based problems, making use of problem-specific knowledge. Particularly, we focus on the well-known Linear Ordering Problem (LOP), taking advantage of the easy computation of the expected and upper bound fitness of partial permutations. Moreover, we introduce a Multi-Objective Optimization approach to deal with the exploration-exploitation dilemma during the tree search. Conducted experiments show that MCTS obtains better results than classical constructive algorithms, though its performance is not obviously comparable to state-of-the-art results. Based on its ability for guiding structured searches, its scalability, convergence and search space coverage, MCTS could open new research trends in the optimization area.\n
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\n \n\n \n \n \n \n \n \n Ideafix: a decision tree-based method for the refinement of variants in FFPE DNA sequencing data.\n \n \n \n \n\n\n \n Tellaetxe-Abete, M.; Calvo, B.; and Lawrie, C.\n\n\n \n\n\n\n NAR Genomics and Bioinformatics, 3(4). 2021.\n \n\n\n\n
\n\n\n\n \n \n \"Ideafix:Website\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Ideafix: a decision tree-based method for the refinement of variants in FFPE DNA sequencing data},\n type = {article},\n year = {2021},\n volume = {3},\n websites = {https://doi.org/10.1093/nargab/lqab092},\n id = {62cc5878-96b6-342b-993a-a5558365881a},\n created = {2021-11-12T08:32:17.291Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:17.291Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {Increasingly, treatment decisions for cancer patients are being made from next-generation sequencing results generated from formalin-fixed and paraffin-embedded (FFPE) biopsies. However, this material is prone to sequence artefacts that cannot be easily identified. In order to address this issue, we designed a machine learning-based algorithm to identify these artefacts using data from \\\\>1 600 000 variants from 27 paired FFPE and fresh-frozen breast cancer samples. Using these data, we assembled a series of variant features and evaluated the classification performance of five machine learning algorithms. Using leave-one-sample-out cross-validation, we found that XGBoost (extreme gradient boosting) and random forest obtained AUC (area under the receiver operating characteristic curve) values \\\\>0.86. Performance was further tested using two independent datasets that resulted in AUC values of 0.96, whereas a comparison with previously published tools resulted in a maximum AUC value of 0.92. The most discriminating features were read pair orientation bias, genomic context and variant allele frequency. In summary, our results show a promising future for the use of these samples in molecular testing. We built the algorithm into an R package called Ideafix (DEAmination FIXing) that is freely available at https://github.com/mmaitenat/ideafix.},\n bibtype = {article},\n author = {Tellaetxe-Abete, Maitena and Calvo, Borja and Lawrie, Charles},\n doi = {10.1093/nargab/lqab092},\n journal = {NAR Genomics and Bioinformatics},\n number = {4}\n}
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\n Increasingly, treatment decisions for cancer patients are being made from next-generation sequencing results generated from formalin-fixed and paraffin-embedded (FFPE) biopsies. However, this material is prone to sequence artefacts that cannot be easily identified. In order to address this issue, we designed a machine learning-based algorithm to identify these artefacts using data from \\\\>1 600 000 variants from 27 paired FFPE and fresh-frozen breast cancer samples. Using these data, we assembled a series of variant features and evaluated the classification performance of five machine learning algorithms. Using leave-one-sample-out cross-validation, we found that XGBoost (extreme gradient boosting) and random forest obtained AUC (area under the receiver operating characteristic curve) values \\\\>0.86. Performance was further tested using two independent datasets that resulted in AUC values of 0.96, whereas a comparison with previously published tools resulted in a maximum AUC value of 0.92. The most discriminating features were read pair orientation bias, genomic context and variant allele frequency. In summary, our results show a promising future for the use of these samples in molecular testing. We built the algorithm into an R package called Ideafix (DEAmination FIXing) that is freely available at https://github.com/mmaitenat/ideafix.\n
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\n \n\n \n \n \n \n \n A Multivariate Time Series Streaming Classifier for Predicting Hard Drive Failures.\n \n \n \n\n\n \n Ircio, J.; Lojo, A.; Lozano, J., A.; and Mori, U.\n\n\n \n\n\n\n IEEE Computational Intelligence Magazine, 17(1): 102-114. 2021.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {A Multivariate Time Series Streaming Classifier for Predicting Hard Drive Failures},\n type = {article},\n year = {2021},\n pages = {102-114},\n volume = {17},\n id = {6c2df7f9-6917-3df3-b7cf-a48de100271a},\n created = {2022-03-15T12:55:47.561Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2022-03-15T12:55:47.561Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Ircio, J and Lojo, A and Lozano, J A and Mori, U},\n journal = {IEEE Computational Intelligence Magazine},\n number = {1}\n}
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\n \n\n \n \n \n \n \n Award Price Estimator for Public Procurement Auctions Using Machine Learning Algorithms: Case Study with Tenders from Spain.\n \n \n \n\n\n \n Garcia Rodriguez, M., J.; Rodriguez Montequin, V.; Aranguren Ubierna, A.; Santana, R.; Sierra Araujo, B.; and Zelaia Jauregi, A.\n\n\n \n\n\n\n STUDIES IN INFORMATICS AND CONTROL, 30(4): 67-76. 2021.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Award Price Estimator for Public Procurement Auctions Using Machine Learning Algorithms: Case Study with Tenders from Spain},\n type = {article},\n year = {2021},\n pages = {67-76},\n volume = {30},\n id = {45dde51b-ad4b-38be-9c4d-e760d5d55684},\n created = {2022-03-30T08:44:10.450Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2022-03-30T08:44:10.450Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {Article},\n private_publication = {false},\n bibtype = {article},\n author = {Garcia Rodriguez, Manuel J and Rodriguez Montequin, Vicente and Aranguren Ubierna, Andoni and Santana, Roberto and Sierra Araujo, Basilio and Zelaia Jauregi, Ana},\n journal = {STUDIES IN INFORMATICS AND CONTROL},\n number = {4}\n}
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\n \n\n \n \n \n \n \n Simulation Framework for Orbit Propagation and Space Trajectory Visualization.\n \n \n \n\n\n \n Shirazi, A.; Ceberio, J.; and Lozano, J., A.\n\n\n \n\n\n\n IEEE Aerospace and Electronic Systems Magazine, 36(8): 4-20. 2021.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Simulation Framework for Orbit Propagation and Space Trajectory Visualization},\n type = {article},\n year = {2021},\n pages = {4-20},\n volume = {36},\n id = {827c097e-0547-384e-a1d2-d7a7149d1e96},\n created = {2022-10-10T08:16:31.489Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2022-10-10T08:16:31.489Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Shirazi, Abolfazl and Ceberio, Josu and Lozano, Jose A},\n doi = {10.1109/MAES.2021.3053121},\n journal = {IEEE Aerospace and Electronic Systems Magazine},\n number = {8}\n}
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\n \n\n \n \n \n \n \n Towards Autonomous Defense of SDN Networks Using MuZero Based Intelligent Agents.\n \n \n \n\n\n \n Gabirondo-López, J.; Egaña, J.; Miguel-Alonso, J.; and Orduna Urrutia, R.\n\n\n \n\n\n\n IEEE Access, 9: 107184-107199. 2021.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Towards Autonomous Defense of SDN Networks Using MuZero Based Intelligent Agents},\n type = {article},\n year = {2021},\n pages = {107184-107199},\n volume = {9},\n id = {d9086516-1646-3610-ba36-91ce0951f55d},\n created = {2022-10-10T11:07:29.733Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2022-10-10T11:07:29.733Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {ARTICLE},\n private_publication = {false},\n bibtype = {article},\n author = {Gabirondo-López, Jon and Egaña, Jon and Miguel-Alonso, Jose and Orduna Urrutia, Raul},\n doi = {10.1109/ACCESS.2021.3100706},\n journal = {IEEE Access}\n}
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\n \n\n \n \n \n \n \n \n On the routing and scalability of MZI-based optical Beneš interconnects.\n \n \n \n \n\n\n \n Kynigos, M.; Pascual, J., A.; Navaridas, J.; Luján, M.; and Goodacre, J.\n\n\n \n\n\n\n Nano Communication Networks, 27: 100337. 2021.\n \n\n\n\n
\n\n\n\n \n \n \"OnWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {On the routing and scalability of MZI-based optical Beneš interconnects},\n type = {article},\n year = {2021},\n keywords = {Circuit switching,Emerging optical and photonic technologies,Network on chip,Optical interconnects},\n pages = {100337},\n volume = {27},\n websites = {https://www.sciencedirect.com/science/article/pii/S187877892030106X},\n id = {bfbb611b-9d83-34f3-be4a-031ab5d784cb},\n created = {2024-01-26T11:05:12.063Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2024-01-26T11:05:12.063Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {Silicon Photonic interconnects are a promising technology for scaling computing systems into the exa-scale domain. However, there exist significant challenges in terms of optical losses and complexity. In this work, we evaluate the applicability of a thermally/electrically tuned Beneš network based on Mach–Zehnder Interferometers for on-chip and inter-chip interconnects as regards its scalability. We examine how insertion loss, laser power and switching energy consumption scale with the number of endpoints. In addition, we propose a set of hardware-inspired routing strategies that leverage the inherent asymmetry present in the switching components. We evaluate a range of network sizes, from 16 up to 256 endpoints, using 8 realistic and synthetic workloads and found very promising results. Our routing strategies offer a reduction in path-dependent insertion loss of up to 35% in the best case, as well as a laser power reduction of 31% for 32 endpoints. In addition, bit-switching energy is reduced by between 8% and 15% using the most efficient routing strategy, depending on the communication workload. We also show that workload execution time can be reduced with the best strategies by 5%–25% in some workloads, while the worst-case increases are at most 3%. Using our routing strategies, we show that under the examined technology parameters, a 32-endpoint interconnect can be considered for the NoC domain in terms of insertion loss and laser power, even when using conservative parameters for the modulator.},\n bibtype = {article},\n author = {Kynigos, Markos and Pascual, Jose A and Navaridas, Javier and Luján, Mikel and Goodacre, John},\n doi = {https://doi.org/10.1016/j.nancom.2020.100337},\n journal = {Nano Communication Networks}\n}
\n
\n\n\n
\n Silicon Photonic interconnects are a promising technology for scaling computing systems into the exa-scale domain. However, there exist significant challenges in terms of optical losses and complexity. In this work, we evaluate the applicability of a thermally/electrically tuned Beneš network based on Mach–Zehnder Interferometers for on-chip and inter-chip interconnects as regards its scalability. We examine how insertion loss, laser power and switching energy consumption scale with the number of endpoints. In addition, we propose a set of hardware-inspired routing strategies that leverage the inherent asymmetry present in the switching components. We evaluate a range of network sizes, from 16 up to 256 endpoints, using 8 realistic and synthetic workloads and found very promising results. Our routing strategies offer a reduction in path-dependent insertion loss of up to 35% in the best case, as well as a laser power reduction of 31% for 32 endpoints. In addition, bit-switching energy is reduced by between 8% and 15% using the most efficient routing strategy, depending on the communication workload. We also show that workload execution time can be reduced with the best strategies by 5%–25% in some workloads, while the worst-case increases are at most 3%. Using our routing strategies, we show that under the examined technology parameters, a 32-endpoint interconnect can be considered for the NoC domain in terms of insertion loss and laser power, even when using conservative parameters for the modulator.\n
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\n \n\n \n \n \n \n \n \n Power and energy efficient routing for Mach-Zehnder interferometer based photonic switches.\n \n \n \n \n\n\n \n Kynigos, M.; Pascual, J., A.; Navaridas, J.; Goodacre, J.; and Lujan, M.\n\n\n \n\n\n\n In Proceedings of the ACM International Conference on Supercomputing, of ICS '21, pages 177-189, 2021. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"PowerWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Power and energy efficient routing for Mach-Zehnder interferometer based photonic switches},\n type = {inproceedings},\n year = {2021},\n keywords = {Mach-Zehnder interferometers,TDM,photonic switches,routing,top-of-rack},\n pages = {177-189},\n websites = {https://doi.org/10.1145/3447818.3460363},\n publisher = {Association for Computing Machinery},\n city = {New York, NY, USA},\n series = {ICS '21},\n id = {a74cecf4-205d-39b4-b973-07fa40721efa},\n created = {2024-01-26T11:06:29.637Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2024-01-26T11:06:29.637Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {Silicon Photonic top-of-rack (ToR) switches are highly desirable for the datacenter (DC) and high-performance computing (HPC) domains for their potential high-bandwidth and energy efficiency. Recently, photonic Beneš switching fabrics based on Mach-Zehnder Interferometers (MZIs) have been proposed as a promising candidate for the internals of high-performance switches. However, state-of-the-art routing algorithms that control these switching fabrics are either computationally complex or unable to provide non-blocking, energy efficient routing permutations.To address this, we propose for the first time a combination of energy efficient routing algorithms and time-division multiplexing (TDM). We evaluate this approach by conducting a simulation-based performance evaluation of a 16x16 Beneš fabric, deployed as a ToR switch, when handling a set of 8 representative workloads from the DC and HPC domains. Our results show that state-of-the-art approaches (circuit switched energy efficient routing algorithms) introduce up to 23% contention in the switching fabric for some workloads, thereby increasing communication time. We show that augmenting the algorithms with TDM can ameliorate switch fabric contention by segmenting communication data and gracefully interleaving the segments, thus reducing communication time by up to 20% in the best case. We also discuss the impact of the TDM segment size, finding that although a 10KB segment size is the most beneficial in reducing communication time, a 100KB segment size offers similar performance while requiring a less stringent path-computation time window. Finally, we assess the impact of TDM on path-dependent insertion loss and switching energy consumption, finding it to be minimal in all cases.},\n bibtype = {inproceedings},\n author = {Kynigos, Markos and Pascual, Jose A and Navaridas, Javier and Goodacre, John and Lujan, Mikel},\n doi = {10.1145/3447818.3460363},\n booktitle = {Proceedings of the ACM International Conference on Supercomputing}\n}
\n
\n\n\n
\n Silicon Photonic top-of-rack (ToR) switches are highly desirable for the datacenter (DC) and high-performance computing (HPC) domains for their potential high-bandwidth and energy efficiency. Recently, photonic Beneš switching fabrics based on Mach-Zehnder Interferometers (MZIs) have been proposed as a promising candidate for the internals of high-performance switches. However, state-of-the-art routing algorithms that control these switching fabrics are either computationally complex or unable to provide non-blocking, energy efficient routing permutations.To address this, we propose for the first time a combination of energy efficient routing algorithms and time-division multiplexing (TDM). We evaluate this approach by conducting a simulation-based performance evaluation of a 16x16 Beneš fabric, deployed as a ToR switch, when handling a set of 8 representative workloads from the DC and HPC domains. Our results show that state-of-the-art approaches (circuit switched energy efficient routing algorithms) introduce up to 23% contention in the switching fabric for some workloads, thereby increasing communication time. We show that augmenting the algorithms with TDM can ameliorate switch fabric contention by segmenting communication data and gracefully interleaving the segments, thus reducing communication time by up to 20% in the best case. We also discuss the impact of the TDM segment size, finding that although a 10KB segment size is the most beneficial in reducing communication time, a 100KB segment size offers similar performance while requiring a less stringent path-computation time window. Finally, we assess the impact of TDM on path-dependent insertion loss and switching energy consumption, finding it to be minimal in all cases.\n
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\n \n\n \n \n \n \n \n \n Towards a framework for fishing route optimization decision support systems: Review of the state-of-the-art and challenges.\n \n \n \n \n\n\n \n Granado, I.; Hernando, L.; Galparsoro, I.; Gabiña, G.; Groba, C.; Prellezo, R.; and Fernandes, J., A.\n\n\n \n\n\n\n Journal of Cleaner Production, 320: 128661. 2021.\n \n\n\n\n
\n\n\n\n \n \n \"TowardsWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Towards a framework for fishing route optimization decision support systems: Review of the state-of-the-art and challenges},\n type = {article},\n year = {2021},\n keywords = {Decision support systems,Exact and heuristic algorithms,Fisheries planning,Route optimization,Ship routing and scheduling,Weather routing},\n pages = {128661},\n volume = {320},\n websites = {https://www.sciencedirect.com/science/article/pii/S0959652621028602},\n id = {d7d1f932-b4cd-321b-9508-2a446c746132},\n created = {2024-02-06T10:43:49.641Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2024-02-06T10:43:49.641Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {Route optimization methods offer an opportunity to the fisheries industry to enhance their efficiency, sustainability, and safety. However, the use of route optimization Decision Support Systems (DSS), which have been widely used in the shipping industry, is limited in the case of fisheries. In the first part, this work describes the fishing routing problems, reviews the state-of-the-art methods applied in the shipping industry, and introduces a general framework for fishing route optimization decision support systems (FRODSS). In the second part, we highlight the existing gap for the application of DSS in fisheries, and how to develop a FRODSS considering the different types of fishing fleets. Finally, and using the diverse Basque fishing fleet as a case study, we conclude that fishing fleets can be summarized into four main groups whose fishing routes could be optimized in a similar way. This characterization is based on their similarities, such us the target species, fishing gear, and the type and distance to the fishing grounds. These four groups are: (i) small-scale coastal fleet; (ii) large-scale pelagic fleet; (iii) large-scale demersal fleet; and (iv) the distant-water fleet. Distant-water vessels are currently the fleet that can more easily benefit from FRODSS, and they are used as an example here. However, the rest of the fleets could also benefit through adequate adaptation to their operation characteristics, driven by their specific fishing gear and target species.},\n bibtype = {article},\n author = {Granado, Igor and Hernando, Leticia and Galparsoro, Ibon and Gabiña, Gorka and Groba, Carlos and Prellezo, Raul and Fernandes, Jose A},\n doi = {https://doi.org/10.1016/j.jclepro.2021.128661},\n journal = {Journal of Cleaner Production}\n}
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\n\n\n
\n Route optimization methods offer an opportunity to the fisheries industry to enhance their efficiency, sustainability, and safety. However, the use of route optimization Decision Support Systems (DSS), which have been widely used in the shipping industry, is limited in the case of fisheries. In the first part, this work describes the fishing routing problems, reviews the state-of-the-art methods applied in the shipping industry, and introduces a general framework for fishing route optimization decision support systems (FRODSS). In the second part, we highlight the existing gap for the application of DSS in fisheries, and how to develop a FRODSS considering the different types of fishing fleets. Finally, and using the diverse Basque fishing fleet as a case study, we conclude that fishing fleets can be summarized into four main groups whose fishing routes could be optimized in a similar way. This characterization is based on their similarities, such us the target species, fishing gear, and the type and distance to the fishing grounds. These four groups are: (i) small-scale coastal fleet; (ii) large-scale pelagic fleet; (iii) large-scale demersal fleet; and (iv) the distant-water fleet. Distant-water vessels are currently the fleet that can more easily benefit from FRODSS, and they are used as an example here. However, the rest of the fleets could also benefit through adequate adaptation to their operation characteristics, driven by their specific fishing gear and target species.\n
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\n  \n 2020\n \n \n (35)\n \n \n
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\n \n\n \n \n \n \n \n Analysis of the transferability and robustness of GANs evolved for Pareto set approximations.\n \n \n \n\n\n \n Garciarena, U.; Mendiburu, A.; and Santana, R.\n\n\n \n\n\n\n Neural Networks, 132: 281-296. 2020.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Analysis of the transferability and robustness of GANs evolved for Pareto set approximations},\n type = {article},\n year = {2020},\n keywords = {Generative adversarial networks,Knowledge transferability,Multi-objective optimization,Neuro-evolution,Pareto front approximation},\n pages = {281-296},\n volume = {132},\n id = {ea3cbb66-31f1-34b2-8f32-e4be095b30ec},\n created = {2021-11-12T08:30:17.067Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:17.067Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {The generative adversarial network (GAN) is a good example of a strong-performing, neural network-based generative model, even though it does have some drawbacks of its own. Mode collapsing and the difficulty in finding the optimal network structure are two of the most concerning issues. In this paper, we address these two issues at the same time by proposing a neuro-evolutionary approach with an agile evaluation method for the fast evolution of robust deep architectures that avoid mode collapsing. The computation of Pareto set approximations with GANs is chosen as a suitable benchmark to evaluate the quality of our approach. Furthermore, we demonstrate the consistency, scalability, and generalization capabilities of the proposed method, which shows its potential applications to many areas. We finally readdress the issue of designing this kind of models by analyzing the characteristics of the best performing GAN specifications, and conclude with a set of general guidelines. This results in a reduction of the many-dimensional problem of structural manual design or automated search.},\n bibtype = {article},\n author = {Garciarena, Unai and Mendiburu, Alexander and Santana, Roberto},\n journal = {Neural Networks}\n}
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\n The generative adversarial network (GAN) is a good example of a strong-performing, neural network-based generative model, even though it does have some drawbacks of its own. Mode collapsing and the difficulty in finding the optimal network structure are two of the most concerning issues. In this paper, we address these two issues at the same time by proposing a neuro-evolutionary approach with an agile evaluation method for the fast evolution of robust deep architectures that avoid mode collapsing. The computation of Pareto set approximations with GANs is chosen as a suitable benchmark to evaluate the quality of our approach. Furthermore, we demonstrate the consistency, scalability, and generalization capabilities of the proposed method, which shows its potential applications to many areas. We finally readdress the issue of designing this kind of models by analyzing the characteristics of the best performing GAN specifications, and conclude with a set of general guidelines. This results in a reduction of the many-dimensional problem of structural manual design or automated search.\n
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\n \n\n \n \n \n \n \n Optimization of Deep Learning Precipitation Models Using Categorical Binary Metrics.\n \n \n \n\n\n \n Larraondo, P., R.; Renzullo, L., J.; Van Dijk, A., I., J., M.; Inza, I.; and Lozano, J., A.\n\n\n \n\n\n\n Journal of Advances in Modeling Earth Systems, 12(5): e2019MS001909. 2020.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Optimization of Deep Learning Precipitation Models Using Categorical Binary Metrics},\n type = {article},\n year = {2020},\n keywords = {categorical indexes,machine learning,modeling,neural networks,precipitation verification},\n pages = {e2019MS001909},\n volume = {12},\n id = {8efab639-c52f-395e-89ed-7c167fdc5077},\n created = {2021-11-12T08:30:21.198Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:21.198Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {Abstract This work introduces a methodology for optimizing neural network models using a combination of continuous and categorical binary indices in the context of precipitation forecasting. Probability of detection and false alarm rate are popular metrics used in the verification of precipitation models. However, machine learning models trained using gradient descent cannot be optimized based on these metrics, as they are not differentiable. We propose an alternative formulation for these categorical indices that are differentiable and we demonstrate how they can be used to optimize the skill of precipitation neural network models defined as a multiobjective optimization problem. To our knowledge, this is the first proposal of a methodology for optimizing weather neural network models based on categorical indices.},\n bibtype = {article},\n author = {Larraondo, Pablo R and Renzullo, Luigi J and Van Dijk, Albert I J M and Inza, Inaki and Lozano, Jose A},\n journal = {Journal of Advances in Modeling Earth Systems},\n number = {5}\n}
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\n Abstract This work introduces a methodology for optimizing neural network models using a combination of continuous and categorical binary indices in the context of precipitation forecasting. Probability of detection and false alarm rate are popular metrics used in the verification of precipitation models. However, machine learning models trained using gradient descent cannot be optimized based on these metrics, as they are not differentiable. We propose an alternative formulation for these categorical indices that are differentiable and we demonstrate how they can be used to optimize the skill of precipitation neural network models defined as a multiobjective optimization problem. To our knowledge, this is the first proposal of a methodology for optimizing weather neural network models based on categorical indices.\n
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\n \n\n \n \n \n \n \n EvoFlow: A Python library for evolving deep neural network architectures in tensorflow.\n \n \n \n\n\n \n Garciarena, U.; Santana, R.; and Mendiburu, A.\n\n\n \n\n\n\n In 2020 IEEE Symposium Series on Computational Intelligence (SSCI), pages 2288-2295, 2020. \n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {EvoFlow: A Python library for evolving deep neural network architectures in tensorflow},\n type = {inproceedings},\n year = {2020},\n pages = {2288-2295},\n id = {d6d25d04-fec4-3989-9115-2b753fe7199e},\n created = {2021-11-12T08:30:33.168Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:33.168Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Garciarena, U and Santana, R and Mendiburu, A},\n doi = {10.1109/SSCI47803.2020.9308214},\n booktitle = {2020 IEEE Symposium Series on Computational Intelligence (SSCI)}\n}
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\n \n\n \n \n \n \n \n Envisioning the Benefits of Back-Drive in Evolutionary Algorithms.\n \n \n \n\n\n \n Garciarena, U.; Mendiburu, A.; and Santana, R.\n\n\n \n\n\n\n In 2020 IEEE Congress on Evolutionary Computation (CEC), pages 1-8, 2020. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Envisioning the Benefits of Back-Drive in Evolutionary Algorithms},\n type = {inproceedings},\n year = {2020},\n pages = {1-8},\n id = {18bd40df-c10b-35b3-9c47-57bfd5173894},\n created = {2021-11-12T08:30:38.359Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:38.359Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Garciarena, Unai and Mendiburu, Alexander and Santana, Roberto},\n booktitle = {2020 IEEE Congress on Evolutionary Computation (CEC)}\n}
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\n \n\n \n \n \n \n \n Decoding and encoding models reveal the role of mental simulation in the brain representation of meaning.\n \n \n \n\n\n \n Soto, D.; Sheikh, U., A.; Mei, N.; and Santana, R.\n\n\n \n\n\n\n Royal Society Open Science, 7(5): 192043. 2020.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Decoding and encoding models reveal the role of mental simulation in the brain representation of meaning},\n type = {article},\n year = {2020},\n pages = {192043},\n volume = {7},\n id = {c8ac4325-9ab3-3b56-98ad-5b408bd05462},\n created = {2021-11-12T08:30:50.136Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:50.136Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = { How the brain representation of conceptual knowledge varies as a function of processing goals, strategies and task-factors remains a key unresolved question in cognitive neuroscience. In the present functional magnetic resonance imaging study, participants were presented with visual words during functional magnetic resonance imaging (fMRI). During shallow processing, participants had to read the items. During deep processing, they had to mentally simulate the features associated with the words. Multivariate classification, informational connectivity and encoding models were used to reveal how the depth of processing determines the brain representation of word meaning. Decoding accuracy in putative substrates of the semantic network was enhanced when the depth processing was high, and the brain representations were more generalizable in semantic space relative to shallow processing contexts. This pattern was observed even in association areas in inferior frontal and parietal cortex. Deep information processing during mental simulation also increased the informational connectivity within key substrates of the semantic network. To further examine the properties of the words encoded in brain activity, we compared computer vision models—associated with the image referents of the words—and word embedding. Computer vision models explained more variance of the brain responses across multiple areas of the semantic network. These results indicate that the brain representation of word meaning is highly malleable by the depth of processing imposed by the task, relies on access to visual representations and is highly distributed, including prefrontal areas previously implicated in semantic control. },\n bibtype = {article},\n author = {Soto, David and Sheikh, Usman Ayub and Mei, Ning and Santana, Roberto},\n journal = {Royal Society Open Science},\n number = {5}\n}
\n
\n\n\n
\n How the brain representation of conceptual knowledge varies as a function of processing goals, strategies and task-factors remains a key unresolved question in cognitive neuroscience. In the present functional magnetic resonance imaging study, participants were presented with visual words during functional magnetic resonance imaging (fMRI). During shallow processing, participants had to read the items. During deep processing, they had to mentally simulate the features associated with the words. Multivariate classification, informational connectivity and encoding models were used to reveal how the depth of processing determines the brain representation of word meaning. Decoding accuracy in putative substrates of the semantic network was enhanced when the depth processing was high, and the brain representations were more generalizable in semantic space relative to shallow processing contexts. This pattern was observed even in association areas in inferior frontal and parietal cortex. Deep information processing during mental simulation also increased the informational connectivity within key substrates of the semantic network. To further examine the properties of the words encoded in brain activity, we compared computer vision models—associated with the image referents of the words—and word embedding. Computer vision models explained more variance of the brain responses across multiple areas of the semantic network. These results indicate that the brain representation of word meaning is highly malleable by the depth of processing imposed by the task, relies on access to visual representations and is highly distributed, including prefrontal areas previously implicated in semantic control. \n
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\n \n\n \n \n \n \n \n An Iterated Local Search Algorithm for the Clonal Deconvolution Problem.\n \n \n \n\n\n \n Tellaetxe-Abete, M.; Calvo, B.; and Lawrie, C.\n\n\n \n\n\n\n In 2020 IEEE Congress on Evolutionary Computation (CEC), pages 1-8, 2020. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {An Iterated Local Search Algorithm for the Clonal Deconvolution Problem},\n type = {inproceedings},\n year = {2020},\n pages = {1-8},\n id = {d84039d0-48de-30a8-8dfc-902e71d5e72c},\n created = {2021-11-12T08:31:06.229Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:06.229Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Tellaetxe-Abete, Maitena and Calvo, Borja and Lawrie, Charles},\n booktitle = {2020 IEEE Congress on Evolutionary Computation (CEC)}\n}
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\n \n\n \n \n \n \n \n Contributions to automatic learning of kernel functions.\n \n \n \n\n\n \n Roman, I.\n\n\n \n\n\n\n Ph.D. Thesis, 2020.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@phdthesis{\n title = {Contributions to automatic learning of kernel functions},\n type = {phdthesis},\n year = {2020},\n institution = {University of the Basque Country (UPV/EHU)},\n id = {2ca66aa7-27ea-3b40-8dfc-3298e67ed0d3},\n created = {2021-11-12T08:31:12.819Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:12.819Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {phdthesis},\n private_publication = {false},\n bibtype = {phdthesis},\n author = {Roman, Ibai}\n}
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\n \n\n \n \n \n \n \n Evolving Gaussian Process Kernels for Translation Editing Effort Estimation.\n \n \n \n\n\n \n Roman, I.; Santana, R.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n In Matsatsinis, N., F.; Marinakis, Y.; and Pardalos, P., editor(s), Learning and Intelligent Optimization, of Lecture Notes in Computer Science, pages 304-318, 2020. Springer International Publishing\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@inproceedings{\n title = {Evolving Gaussian Process Kernels for Translation Editing Effort Estimation},\n type = {inproceedings},\n year = {2020},\n keywords = {Evolutionary search,Gaussian Processes,Genetic Programming,Kernel selection,Quality Estimation},\n pages = {304-318},\n publisher = {Springer International Publishing},\n series = {Lecture Notes in Computer Science},\n id = {4d716b75-7cd0-3c06-b69e-fe603d9a7813},\n created = {2021-11-12T08:31:22.146Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:22.146Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {In many Natural Language Processing problems the combination of machine learning and optimization techniques is essential. One of these problems is estimating the effort required to improve, under direct human supervision, a text that has been translated using a machine translation method. Recent developments in this area have shown that Gaussian Processes can be accurate for post-editing effort prediction. However, the Gaussian Process kernel has to be chosen in advance, and this choice influences the quality of the prediction. In this paper, we propose a Genetic Programming algorithm to evolve kernels for Gaussian Processes. We show that the combination of evolutionary optimization and Gaussian Processes removes the need for a-priori specification of the kernel choice, and achieves predictions that, in many cases, outperform those obtained with fixed kernels.},\n bibtype = {inproceedings},\n author = {Roman, Ibai and Santana, Roberto and Mendiburu, Alexander and Lozano, Jose A},\n editor = {Matsatsinis, Nikolaos F and Marinakis, Yannis and Pardalos, Panos},\n booktitle = {Learning and Intelligent Optimization}\n}
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\n In many Natural Language Processing problems the combination of machine learning and optimization techniques is essential. One of these problems is estimating the effort required to improve, under direct human supervision, a text that has been translated using a machine translation method. Recent developments in this area have shown that Gaussian Processes can be accurate for post-editing effort prediction. However, the Gaussian Process kernel has to be chosen in advance, and this choice influences the quality of the prediction. In this paper, we propose a Genetic Programming algorithm to evolve kernels for Gaussian Processes. We show that the combination of evolutionary optimization and Gaussian Processes removes the need for a-priori specification of the kernel choice, and achieves predictions that, in many cases, outperform those obtained with fixed kernels.\n
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\n \n\n \n \n \n \n \n Tool-Path Problem in Direct Energy Deposition Metal-Additive Manufacturing: Sequence Strategy Generation.\n \n \n \n\n\n \n Murua, M.; Suárez, A.; Galar, D.; and Santana, R.\n\n\n \n\n\n\n IEEE Access, 8: 91574-91585. 2020.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Tool-Path Problem in Direct Energy Deposition Metal-Additive Manufacturing: Sequence Strategy Generation},\n type = {article},\n year = {2020},\n keywords = {Additive manufacturing,Frequency division multiplexing,Optimization,Process planning,Three-dimensional printing,Tools,Wires,direct energy deposition,multi-objective optimization,tool-path generation},\n pages = {91574-91585},\n volume = {8},\n id = {64e15a70-c62c-397f-abc3-308efb0076cf},\n created = {2021-11-12T08:31:28.059Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:28.059Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {The tool-path problem has been extensively studied in manufacturing technologies, as it has a considerable impact on production time. Additive manufacturing is one of these technologies; it takes time to fabricate parts, so the selection of optimal tool-paths is critical. This research analyzes the tool-path problem in the direct energy deposition technology; it introduces the main processes, and analyzes the characteristics of tool-path problem. It explains the approaches applied in the literature to solve the problem; as these are mainly geometric approximations, they are far from optimal. Based on this analysis, this paper introduces a mathematical framework for direct energy deposition and a novel problem called sequence strategy generation. Finally, it solves the problem using a benchmark for several different parts. The results reveal that the approach can be applied to parts with different characteristics, and the solution to the sequence strategy problem can be used to generate tool-paths.},\n bibtype = {article},\n author = {Murua, M and Suárez, A and Galar, D and Santana, R},\n journal = {IEEE Access}\n}
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\n The tool-path problem has been extensively studied in manufacturing technologies, as it has a considerable impact on production time. Additive manufacturing is one of these technologies; it takes time to fabricate parts, so the selection of optimal tool-paths is critical. This research analyzes the tool-path problem in the direct energy deposition technology; it introduces the main processes, and analyzes the characteristics of tool-path problem. It explains the approaches applied in the literature to solve the problem; as these are mainly geometric approximations, they are far from optimal. Based on this analysis, this paper introduces a mathematical framework for direct energy deposition and a novel problem called sequence strategy generation. Finally, it solves the problem using a benchmark for several different parts. The results reveal that the approach can be applied to parts with different characteristics, and the solution to the sequence strategy problem can be used to generate tool-paths.\n
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\n \n\n \n \n \n \n \n Using Convolutional Neural Network for Chest X-ray Image classification.\n \n \n \n\n\n \n Sorić, M.; Pongrac, D.; and Inza, I.\n\n\n \n\n\n\n In 2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO), pages 1771-1776, 2020. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Using Convolutional Neural Network for Chest X-ray Image classification},\n type = {inproceedings},\n year = {2020},\n pages = {1771-1776},\n id = {080b24e3-32bb-379e-b437-4313bdfe5fa6},\n created = {2021-11-12T08:31:29.236Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:29.236Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Sorić, M and Pongrac, D and Inza, I},\n booktitle = {2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO)}\n}
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\n \n\n \n \n \n \n \n In-depth analysis of SVM kernel learning and its components.\n \n \n \n\n\n \n Roman, I.; Santana, R.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n Neural Computing and Applications. 5 2020.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{\n title = {In-depth analysis of SVM kernel learning and its components},\n type = {article},\n year = {2020},\n month = {5},\n id = {c49f3cd8-a348-32fc-95f3-6ead19075d62},\n created = {2021-11-12T08:31:45.210Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:45.210Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {The performance of support vector machines in nonlinearly separable classification problems strongly relies on the kernel function. Toward an automatic machine learning approach for this technique, many research outputs have been produced dealing with the challenge of automatic learning of good-performing kernels for support vector machines. However, these works have been carried out without a thorough analysis of the set of components that influence the behavior of support vector machines and their interaction with the kernel. These components are related in an intricate way and it is difficult to provide a comprehensible analysis of their joint effect. In this paper, we try to fill this gap introducing the necessary steps in order to understand these interactions and provide clues for the research community to know where to place the emphasis. First of all, we identify all the factors that affect the final performance of support vector machines in relation to the elicitation of kernels. Next, we analyze the factors independently or in pairs and study the influence each component has on the final classification performance, providing recommendations and insights into the kernel setting for support vector machines.},\n bibtype = {article},\n author = {Roman, Ibai and Santana, Roberto and Mendiburu, Alexander and Lozano, Jose A},\n journal = {Neural Computing and Applications}\n}
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\n The performance of support vector machines in nonlinearly separable classification problems strongly relies on the kernel function. Toward an automatic machine learning approach for this technique, many research outputs have been produced dealing with the challenge of automatic learning of good-performing kernels for support vector machines. However, these works have been carried out without a thorough analysis of the set of components that influence the behavior of support vector machines and their interaction with the kernel. These components are related in an intricate way and it is difficult to provide a comprehensible analysis of their joint effect. In this paper, we try to fill this gap introducing the necessary steps in order to understand these interactions and provide clues for the research community to know where to place the emphasis. First of all, we identify all the factors that affect the final performance of support vector machines in relation to the elicitation of kernels. Next, we analyze the factors independently or in pairs and study the influence each component has on the final classification performance, providing recommendations and insights into the kernel setting for support vector machines.\n
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\n \n\n \n \n \n \n \n Automatic Structural Search for Multi-task Learning VALPs.\n \n \n \n\n\n \n Garciarena, U.; Mendiburu, A.; and Santana, R.\n\n\n \n\n\n\n In International Conference on Optimization and Learning, pages 25-36, 2020. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Automatic Structural Search for Multi-task Learning VALPs},\n type = {inproceedings},\n year = {2020},\n pages = {25-36},\n id = {86502aa2-e4b2-3c13-8d8d-fbea03a8f6f7},\n created = {2021-11-12T08:31:46.785Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:46.785Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Garciarena, Unai and Mendiburu, Alexander and Santana, Roberto},\n booktitle = {International Conference on Optimization and Learning}\n}
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\n \n\n \n \n \n \n \n Survey of Network Intrusion Detection Methods from the Perspective of the Knowledge Discovery in Databases Process.\n \n \n \n\n\n \n Coronado, B., M.; Mori, U.; Mendiburu, A.; and Miguel-Alonso, J.\n\n\n \n\n\n\n IEEE Transactions on Network and Service Management,1. 2020.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Survey of Network Intrusion Detection Methods from the Perspective of the Knowledge Discovery in Databases Process},\n type = {article},\n year = {2020},\n keywords = {Data mining;Intrusion detection;Detectors;Feature},\n pages = {1},\n id = {d7cdd15b-0160-36bc-8d52-12de4fb1b2ac},\n created = {2021-11-12T08:31:52.526Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:52.526Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {The identification of network attacks which target information and communication systems has been a focus of the research community for years. Network intrusion detection is a complex problem which presents a diverse number of challenges. Many attacks currently remain undetected, while newer ones emerge due to the proliferation of connected devices and the evolution of communication technology. In this survey, we review the methods that have been applied to network data with the purpose of developing an intrusion detector, but contrary to previous reviews in the area, we analyze them from the perspective of the Knowledge Discovery in Databases (KDD) process. As such, we discuss the techniques used for the collecion, preprocessing and transformation of the data, as well as the data mining and evaluation methods. We also present the characteristics and motivations behind the use of each of these techniques and propose more adequate and up-to-date taxonomies and definitions for intrusion detectors based on the terminology used in the area of data mining and KDD. Special importance is given to the evaluation procedures followed to assess the detectors, discussing their applicability in current, real networks. Finally, as a result of this literature review, we investigate some open issues which will need to be considered for further research in the area of network security.},\n bibtype = {article},\n author = {Coronado, B M and Mori, U and Mendiburu, A and Miguel-Alonso, J},\n journal = {IEEE Transactions on Network and Service Management}\n}
\n
\n\n\n
\n The identification of network attacks which target information and communication systems has been a focus of the research community for years. Network intrusion detection is a complex problem which presents a diverse number of challenges. Many attacks currently remain undetected, while newer ones emerge due to the proliferation of connected devices and the evolution of communication technology. In this survey, we review the methods that have been applied to network data with the purpose of developing an intrusion detector, but contrary to previous reviews in the area, we analyze them from the perspective of the Knowledge Discovery in Databases (KDD) process. As such, we discuss the techniques used for the collecion, preprocessing and transformation of the data, as well as the data mining and evaluation methods. We also present the characteristics and motivations behind the use of each of these techniques and propose more adequate and up-to-date taxonomies and definitions for intrusion detectors based on the terminology used in the area of data mining and KDD. Special importance is given to the evaluation procedures followed to assess the detectors, discussing their applicability in current, real networks. Finally, as a result of this literature review, we investigate some open issues which will need to be considered for further research in the area of network security.\n
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\n \n\n \n \n \n \n \n Bayesian Optimization Approaches for Massively Multi-modal Problems.\n \n \n \n\n\n \n Roman, I.; Mendiburu, A.; Santana, R.; and Lozano, J., A.\n\n\n \n\n\n\n In Matsatsinis, N., F.; Marinakis, Y.; and Pardalos, P., editor(s), Learning and Intelligent Optimization, of Lecture Notes in Computer Science, pages 383-397, 2020. Springer International Publishing\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Bayesian Optimization Approaches for Massively Multi-modal Problems},\n type = {inproceedings},\n year = {2020},\n keywords = {Bayesian Optimization,Gaussian processes,Multi-modal Optimization},\n pages = {383-397},\n publisher = {Springer International Publishing},\n series = {Lecture Notes in Computer Science},\n id = {f03e01e2-7c2d-3b1a-8301-a2c88a593713},\n created = {2021-11-12T08:32:01.484Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:01.484Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {The optimization of massively multi-modal functions is a challenging task, particularly for problems where the search space can lead the optimization process to local optima. While evolutionary algorithms have been extensively investigated for these optimization problems, Bayesian Optimization algorithms have not been explored to the same extent. In this paper, we study the behavior of Bayesian Optimization as part of a hybrid approach for solving several massively multi-modal functions. We use well-known benchmarks and metrics to evaluate how different variants of Bayesian Optimization deal with multi-modality.},\n bibtype = {inproceedings},\n author = {Roman, Ibai and Mendiburu, Alexander and Santana, Roberto and Lozano, Jose A},\n editor = {Matsatsinis, Nikolaos F and Marinakis, Yannis and Pardalos, Panos},\n booktitle = {Learning and Intelligent Optimization}\n}
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\n The optimization of massively multi-modal functions is a challenging task, particularly for problems where the search space can lead the optimization process to local optima. While evolutionary algorithms have been extensively investigated for these optimization problems, Bayesian Optimization algorithms have not been explored to the same extent. In this paper, we study the behavior of Bayesian Optimization as part of a hybrid approach for solving several massively multi-modal functions. We use well-known benchmarks and metrics to evaluate how different variants of Bayesian Optimization deal with multi-modality.\n
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\n \n\n \n \n \n \n \n \n Constrained Combinatorial Optimization with Reinforcement Learning.\n \n \n \n \n\n\n \n Solozabal, R.; Ceberio, J.; and Takác, M.\n\n\n \n\n\n\n CoRR, abs/2006.1. 2020.\n \n\n\n\n
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\n 
@article{\n title = {Constrained Combinatorial Optimization with Reinforcement Learning},\n type = {article},\n year = {2020},\n volume = {abs/2006.1},\n websites = {https://arxiv.org/abs/2006.11984},\n id = {1b970dee-5495-35dc-b49e-889c76372767},\n created = {2021-11-12T08:32:03.444Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:03.444Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Solozabal, Ruben and Ceberio, Josu and Takác, Martin},\n journal = {CoRR}\n}
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\n \n\n \n \n \n \n \n Alternative Representations for Codifying Solutions in Permutation-Based Problems.\n \n \n \n\n\n \n Malagon, M.; Irurozki, E.; and Ceberio, J.\n\n\n \n\n\n\n In 2020 IEEE Congress on Evolutionary Computation (CEC), pages 1-8, 2020. \n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Alternative Representations for Codifying Solutions in Permutation-Based Problems},\n type = {inproceedings},\n year = {2020},\n pages = {1-8},\n id = {1e6f5078-981a-3318-a2c4-d9ca0a7b68d7},\n created = {2021-11-12T08:32:04.267Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:04.267Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Malagon, Mikel and Irurozki, Ekhine and Ceberio, Josu},\n doi = {10.1109/CEC48606.2020.9185678},\n booktitle = {2020 IEEE Congress on Evolutionary Computation (CEC)}\n}
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\n \n\n \n \n \n \n \n Gradient search in the space of permutations.\n \n \n \n\n\n \n Santucci, V.; Ceberio, J.; and Baioletti, M.\n\n\n \n\n\n\n In Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, 2020. ACM\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Gradient search in the space of permutations},\n type = {inproceedings},\n year = {2020},\n publisher = {ACM},\n city = {New York, NY, USA},\n id = {b141ee16-5863-3966-b12a-a8331db7b968},\n created = {2021-11-12T08:32:04.534Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:04.534Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Santucci, Valentino and Ceberio, Josu and Baioletti, Marco},\n doi = {10.1145/3377929.3398094},\n booktitle = {Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion}\n}
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\n \n\n \n \n \n \n \n Exploring Gaps in DeepFool in Search of More Effective Adversarial Perturbations.\n \n \n \n\n\n \n Vadillo, J.; Santana, R.; and Lozano, J., A.\n\n\n \n\n\n\n 2020.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inbook{\n type = {inbook},\n year = {2020},\n id = {00b4b04a-d25d-3366-b775-0198ee53f6e5},\n created = {2021-11-12T08:32:05.372Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:05.372Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {incollection},\n private_publication = {false},\n bibtype = {inbook},\n author = {Vadillo, Jon and Santana, Roberto and Lozano, Jose A},\n doi = {10.1007/978-3-030-64580-9_18},\n chapter = {Exploring Gaps in DeepFool in Search of More Effective Adversarial Perturbations}\n}
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\n \n\n \n \n \n \n \n \n An Adaptive Neuroevolution-Based Hyperheuristic.\n \n \n \n \n\n\n \n Arza, E.; Ceberio, J.; Pérez, A.; and Irurozki, E.\n\n\n \n\n\n\n In Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, pages 111-112, 2020. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"AnWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {An Adaptive Neuroevolution-Based Hyperheuristic},\n type = {inproceedings},\n year = {2020},\n keywords = {hyperheuristic,neuroevolution,optimization,transfer learning},\n pages = {111-112},\n websites = {https://doi.org/10.1145/3377929.3389937},\n publisher = {Association for Computing Machinery},\n city = {New York, NY, USA},\n id = {aea39649-0346-3ee8-99c2-2ab956840371},\n created = {2021-11-12T08:32:06.001Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:06.001Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {According to the No-Free-Lunch theorem, an algorithm that performs efficiently on any type of problem does not exist. In this sense, algorithms that exploit problem-specific knowledge usually outperform more generic approaches, at the cost of a more complex design and parameter tuning process. Trying to combine the best of both worlds, the field of hyperheuristics investigates the automatized generation and hybridization of heuristic algorithms. In this paper, we propose a neuroevolution-based hyperheuristic approach. Particularly, we develop a population-based hyperheuristic algorithm that first trains a neural network on an instance of a problem and then uses the trained neural network to control how and which low-level operators are applied to each of the solutions when optimizing different problem instances. The trained neural network maps the state of the optimization process to the operations to be applied to the solutions in the population at each generation.},\n bibtype = {inproceedings},\n author = {Arza, Etor and Ceberio, Josu and Pérez, Aritz and Irurozki, Ekhiñe},\n doi = {10.1145/3377929.3389937},\n booktitle = {Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion}\n}
\n
\n\n\n
\n According to the No-Free-Lunch theorem, an algorithm that performs efficiently on any type of problem does not exist. In this sense, algorithms that exploit problem-specific knowledge usually outperform more generic approaches, at the cost of a more complex design and parameter tuning process. Trying to combine the best of both worlds, the field of hyperheuristics investigates the automatized generation and hybridization of heuristic algorithms. In this paper, we propose a neuroevolution-based hyperheuristic approach. Particularly, we develop a population-based hyperheuristic algorithm that first trains a neural network on an instance of a problem and then uses the trained neural network to control how and which low-level operators are applied to each of the solutions when optimizing different problem instances. The trained neural network maps the state of the optimization process to the operations to be applied to the solutions in the population at each generation.\n
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\n \n\n \n \n \n \n \n Kernels of Mallows Models under the Hamming Distance for solving the Quadratic Assignment Problem.\n \n \n \n\n\n \n Arza, E.; Pérez, A.; Irurozki, E.; and Ceberio, J.\n\n\n \n\n\n\n Swarm and Evolutionary Computation, 59. 2020.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Kernels of Mallows Models under the Hamming Distance for solving the Quadratic Assignment Problem},\n type = {article},\n year = {2020},\n volume = {59},\n id = {dcf03edb-c505-3b84-86df-526d73d69c19},\n created = {2021-11-12T08:32:07.046Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:07.046Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Arza, Etor and Pérez, Aritz and Irurozki, Ekhiñe and Ceberio, Josu},\n doi = {10.1016/j.swevo.2020.100740},\n journal = {Swarm and Evolutionary Computation}\n}
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\n \n\n \n \n \n \n \n Virtual Network Function Placement Optimization With Deep Reinforcement Learning.\n \n \n \n\n\n \n Solozabal, R.; Ceberio, J.; Sanchoyerto, A.; Zabala, L.; Blanco, B.; and Liberal, F.\n\n\n \n\n\n\n IEEE Journal on Selected Areas in Communications, 38(2). 2020.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Virtual Network Function Placement Optimization With Deep Reinforcement Learning},\n type = {article},\n year = {2020},\n volume = {38},\n id = {efbc247d-5fa7-314f-a415-92a6bf9e80a8},\n created = {2021-11-12T08:32:07.319Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:07.319Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Solozabal, Ruben and Ceberio, Josu and Sanchoyerto, Aitor and Zabala, Luis and Blanco, Bego and Liberal, Fidel},\n doi = {10.1109/JSAC.2019.2959183},\n journal = {IEEE Journal on Selected Areas in Communications},\n number = {2}\n}
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\n \n\n \n \n \n \n \n Investigating RNNs for vehicle volume forecasting in service stations.\n \n \n \n\n\n \n Khargharia, H., S.; Santana, R.; Shakya, S.; Ainslie, R.; and Owusu, G.\n\n\n \n\n\n\n In 2020 IEEE Symposium Series on Computational Intelligence (SSCI), pages 2625-2632, 2020. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Investigating RNNs for vehicle volume forecasting in service stations},\n type = {inproceedings},\n year = {2020},\n pages = {2625-2632},\n id = {51f399c4-def1-3c07-b2e8-255219319c47},\n created = {2021-11-12T08:32:08.213Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:08.213Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Khargharia, Himadri Sikhar and Santana, Roberto and Shakya, Siddhartha and Ainslie, Russell and Owusu, Gilbert},\n booktitle = {2020 IEEE Symposium Series on Computational Intelligence (SSCI)}\n}
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\n \n\n \n \n \n \n \n Mutual information based feature subset selection in multivariate time series classification.\n \n \n \n\n\n \n Ircio, J.; Lojo, A.; Mori, U.; and Lozano, J., A.\n\n\n \n\n\n\n Pattern Recognition, 108: 107525. 2020.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Mutual information based feature subset selection in multivariate time series classification},\n type = {article},\n year = {2020},\n pages = {107525},\n volume = {108},\n publisher = {Elsevier},\n id = {fa0d9c62-d615-3be8-b279-05f897ffa94b},\n created = {2021-11-12T08:32:08.470Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:08.470Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Ircio, Josu and Lojo, Aizea and Mori, Usue and Lozano, Jose A},\n journal = {Pattern Recognition}\n}
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\n \n\n \n \n \n \n \n An efficient Split-Merge re-start for the K-means algorithm.\n \n \n \n\n\n \n Capo, M.; Perez, A.; and Lozano, J., A., A.\n\n\n \n\n\n\n IEEE Transactions on Knowledge and Data Engineering. 2020.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {An efficient Split-Merge re-start for the K-means algorithm},\n type = {article},\n year = {2020},\n publisher = {IEEE},\n id = {04f238e1-faf6-33fa-8691-f0922e4b9e19},\n created = {2021-11-12T08:32:08.771Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:08.771Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Capo, Marco and Perez, Aritz and Lozano, Jose A Antonio},\n journal = {IEEE Transactions on Knowledge and Data Engineering}\n}
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\n \n\n \n \n \n \n \n Robust image classification against adversarial attacks using elastic similarity measures between edge count sequences.\n \n \n \n\n\n \n Oregi, I.; Del Ser, J.; Pérez, A.; and Lozano, J., A.\n\n\n \n\n\n\n Neural Networks, 128: 61-72. 2020.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{\n title = {Robust image classification against adversarial attacks using elastic similarity measures between edge count sequences},\n type = {article},\n year = {2020},\n pages = {61-72},\n volume = {128},\n publisher = {Elsevier},\n id = {8f718334-70b8-3892-acdd-f0bb3aa5b6d7},\n created = {2021-11-12T08:32:10.183Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:10.183Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Oregi, Izaskun and Del Ser, Javier and Pérez, Aritz and Lozano, José A},\n journal = {Neural Networks}\n}
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\n \n\n \n \n \n \n \n Dynamic programming operators for the bi-objective Traveling Thief Problem.\n \n \n \n\n\n \n Santana, R.; and Shakya, S.\n\n\n \n\n\n\n In 2020 IEEE Congress on Evolutionary Computation (CEC), pages 1-8, 2020. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{\n title = {Dynamic programming operators for the bi-objective Traveling Thief Problem},\n type = {inproceedings},\n year = {2020},\n pages = {1-8},\n id = {db6ed4cb-fcf3-3685-81ac-c2760c9ca9e0},\n created = {2021-11-12T08:32:11.778Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:11.778Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Santana, Roberto and Shakya, Siddhartha},\n booktitle = {2020 IEEE Congress on Evolutionary Computation (CEC)}\n}
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\n \n\n \n \n \n \n \n Merge nondominated sorting algorithm for many-objective optimization.\n \n \n \n\n\n \n Moreno, J.; Rodriguez, D.; Nebro, A., J.; and Lozano, J., A.\n\n\n \n\n\n\n IEEE Transactions on Cybernetics. 2020.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{\n title = {Merge nondominated sorting algorithm for many-objective optimization},\n type = {article},\n year = {2020},\n publisher = {IEEE},\n id = {bbfaa6a6-5fb5-3dc9-a473-fb42e36ccb48},\n created = {2021-11-12T08:32:12.062Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:12.062Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Moreno, Javier and Rodriguez, Daniel and Nebro, Antonio J and Lozano, Jose A},\n journal = {IEEE Transactions on Cybernetics}\n}
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\n \n\n \n \n \n \n \n Multi-objective Approach to the Protein Structure Prediction Problem.\n \n \n \n\n\n \n Lima, R., H., R.; Fontoura, V.; Pozo, A.; and Santana, R.\n\n\n \n\n\n\n Evolutionary Multi-Objective System Design, pages 151-169. Chapman and Hall/CRC, 2020.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inbook{\n type = {inbook},\n year = {2020},\n pages = {151-169},\n publisher = {Chapman and Hall/CRC},\n id = {8740f635-a37f-38cd-a7a6-0db73eb06971},\n created = {2021-11-12T08:32:12.334Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:12.334Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {incollection},\n private_publication = {false},\n bibtype = {inbook},\n author = {Lima, Ricardo H R and Fontoura, Vidal and Pozo, Aurora and Santana, Roberto},\n chapter = {Multi-objective Approach to the Protein Structure Prediction Problem},\n title = {Evolutionary Multi-Objective System Design}\n}
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\n \n\n \n \n \n \n \n A Symmetric grammar approach for designing segmentation models.\n \n \n \n\n\n \n Lima, R., H., R.; Pozo, A.; Mendiburu, A.; and Santana, R.\n\n\n \n\n\n\n In 2020 IEEE Congress on Evolutionary Computation (CEC), pages 1-8, 2020. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{\n title = {A Symmetric grammar approach for designing segmentation models},\n type = {inproceedings},\n year = {2020},\n pages = {1-8},\n id = {06fef90e-cbd8-36b5-841e-fe99b8ddbba2},\n created = {2021-11-12T08:32:13.069Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:13.069Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Lima, Ricardo H R and Pozo, Aurora and Mendiburu, Alexander and Santana, Roberto},\n booktitle = {2020 IEEE Congress on Evolutionary Computation (CEC)}\n}
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\n \n\n \n \n \n \n \n A cheap feature selection approach for the K-means algorithm.\n \n \n \n\n\n \n Capó, M.; Pérez, A.; and Lozano, J., A.\n\n\n \n\n\n\n IEEE Transactions on Neural Networks and Learning Systems, 32(5): 2195-2208. 2020.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{\n title = {A cheap feature selection approach for the K-means algorithm},\n type = {article},\n year = {2020},\n pages = {2195-2208},\n volume = {32},\n publisher = {IEEE},\n id = {eef3a0c8-343b-313a-8605-0eec82f20010},\n created = {2021-11-12T08:32:13.617Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:13.617Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Capó, Marco and Pérez, Aritz and Lozano, Jose A},\n journal = {IEEE Transactions on Neural Networks and Learning Systems},\n number = {5}\n}
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\n \n\n \n \n \n \n \n Evaluation of the temperature and time in centrifugation-assisted freeze concentration.\n \n \n \n\n\n \n Santana, T.; Moreno, J.; Petzold, G.; Santana, R.; and Sáez-Trautmann, G.\n\n\n \n\n\n\n Applied Sciences, 10(24): 9130. 2020.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Evaluation of the temperature and time in centrifugation-assisted freeze concentration},\n type = {article},\n year = {2020},\n pages = {9130},\n volume = {10},\n publisher = {Multidisciplinary Digital Publishing Institute},\n id = {70f57f96-d940-3b08-93e3-7b43d3c1f86f},\n created = {2021-11-12T08:32:14.159Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:14.159Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Santana, Tamara and Moreno, Jorge and Petzold, Guillermo and Santana, Roberto and Sáez-Trautmann, Guido},\n journal = {Applied Sciences},\n number = {24}\n}
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\n \n\n \n \n \n \n \n Transfer learning in hierarchical dialogue topic classification with neural networks.\n \n \n \n\n\n \n Montenegro, C.; Santana, R.; and Lozano, J., A.\n\n\n \n\n\n\n In 2020 International Joint Conference on Neural Networks (IJCNN), pages 1-8, 2020. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Transfer learning in hierarchical dialogue topic classification with neural networks},\n type = {inproceedings},\n year = {2020},\n pages = {1-8},\n id = {9048eb4b-b982-3490-9a7b-a747134293ac},\n created = {2021-11-12T08:32:14.497Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:14.497Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Montenegro, Cesar and Santana, Roberto and Lozano, Jose A},\n booktitle = {2020 International Joint Conference on Neural Networks (IJCNN)}\n}
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\n \n\n \n \n \n \n \n Identifying common treatments from Electronic Health Records with missing information. An application to breast cancer.\n \n \n \n\n\n \n Zaballa, O.; Pérez, A.; Gómez Inhiesto, E.; Acaiturri Ayesta, T.; and Lozano, J., A.\n\n\n \n\n\n\n PLoS One, 15(12): e0244004. 2020.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Identifying common treatments from Electronic Health Records with missing information. An application to breast cancer},\n type = {article},\n year = {2020},\n pages = {e0244004},\n volume = {15},\n publisher = {Public Library of Science San Francisco, CA USA},\n id = {b30c20a4-30fd-32e9-90c4-9d2f8848dd86},\n created = {2021-11-12T08:32:15.081Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:15.081Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Zaballa, Onintze and Pérez, Aritz and Gómez Inhiesto, Elisa and Acaiturri Ayesta, Teresa and Lozano, Jose A},\n journal = {PLoS One},\n number = {12}\n}
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\n \n\n \n \n \n \n \n Survey of Network Intrusion Detection Methods From the Perspective of the Knowledge Discovery in Databases Process.\n \n \n \n\n\n \n Molina-Coronado, B.; Mori, U.; Mendiburu, A.; and Miguel-Alonso, J.\n\n\n \n\n\n\n IEEE Transactions on Network and Service Management, 17(4): 2451-2479. 2020.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Survey of Network Intrusion Detection Methods From the Perspective of the Knowledge Discovery in Databases Process},\n type = {article},\n year = {2020},\n pages = {2451-2479},\n volume = {17},\n id = {ff0510e5-cd16-352f-a600-287b36ab3538},\n created = {2022-10-10T11:07:36.236Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2022-10-10T11:07:36.236Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {ARTICLE},\n private_publication = {false},\n bibtype = {article},\n author = {Molina-Coronado, Borja and Mori, Usue and Mendiburu, Alexander and Miguel-Alonso, Jose},\n doi = {10.1109/TNSM.2020.3016246},\n journal = {IEEE Transactions on Network and Service Management},\n number = {4}\n}
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\n \n\n \n \n \n \n \n \n Journey to the center of the linear ordering problem.\n \n \n \n \n\n\n \n Hernando, L.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n In Proceedings of the 2020 Genetic and Evolutionary Computation Conference, of GECCO '20, pages 201-209, 2020. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"JourneyWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@inproceedings{\n title = {Journey to the center of the linear ordering problem},\n type = {inproceedings},\n year = {2020},\n keywords = {landscape analysis,linear ordering problem,local optima,permutation-based combinatorial optimization problems},\n pages = {201-209},\n websites = {https://doi.org/10.1145/3377930.3390241},\n publisher = {Association for Computing Machinery},\n city = {New York, NY, USA},\n series = {GECCO '20},\n id = {f548ee3e-7d61-3bc8-bccd-acfc16404e2e},\n created = {2024-02-06T10:45:13.586Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2024-02-06T10:45:13.586Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {A number of local search based algorithms have been designed to escape from the local optima, such as, iterated local search or variable neighborhood search. The neighborhood chosen for the local search as well as the escape technique play a key role in the performance of these algorithms. Of course, a specific strategy has a different effect on distinct problems or instances. In this paper, we focus on a permutation-based combinatorial optimization problem: the linear ordering problem. We provide a theoretical landscape analysis for the adjacent swap, the swap and the insert neighborhoods. By making connections to other different problems found in the Combinatorics field, we prove that there are some moves in the local optima that will necessarily return a worse or equal solution. The number of these non-better solutions that could be avoided by the escape techniques is considerably large with respect to the number of neighbors. This is a valuable information that can be included in any of those algorithms designed to escape from the local optima, increasing their efficiency.},\n bibtype = {inproceedings},\n author = {Hernando, Leticia and Mendiburu, Alexander and Lozano, Jose A},\n doi = {10.1145/3377930.3390241},\n booktitle = {Proceedings of the 2020 Genetic and Evolutionary Computation Conference}\n}
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\n A number of local search based algorithms have been designed to escape from the local optima, such as, iterated local search or variable neighborhood search. The neighborhood chosen for the local search as well as the escape technique play a key role in the performance of these algorithms. Of course, a specific strategy has a different effect on distinct problems or instances. In this paper, we focus on a permutation-based combinatorial optimization problem: the linear ordering problem. We provide a theoretical landscape analysis for the adjacent swap, the swap and the insert neighborhoods. By making connections to other different problems found in the Combinatorics field, we prove that there are some moves in the local optima that will necessarily return a worse or equal solution. The number of these non-better solutions that could be avoided by the escape techniques is considerably large with respect to the number of neighbors. This is a valuable information that can be included in any of those algorithms designed to escape from the local optima, increasing their efficiency.\n
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\n  \n 2019\n \n \n (40)\n \n \n
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\n \n\n \n \n \n \n \n On the effects of allocation strategies for exascale computing systems with distributed storage and unified interconnects.\n \n \n \n\n\n \n Pascual, J., A.; Lant, J.; Concatto, C.; Attwood, A.; Navaridas, J.; Luján, M.; and Goodacre, J.\n\n\n \n\n\n\n Concurrency and Computation: Practice and Experience, 31(21): e4784. 2019.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {On the effects of allocation strategies for exascale computing systems with distributed storage and unified interconnects},\n type = {article},\n year = {2019},\n keywords = {inter-processor communications,near-data computing,resource allocation,scheduling,storage traffic},\n pages = {e4784},\n volume = {31},\n id = {ffb3d43b-c908-372e-9fdd-92edaf5eb717},\n created = {2021-11-12T08:30:10.903Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:10.903Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {Summary The convergence between computing- and data-centric workloads and platforms is imposing new challenges on how to best use the resources of modern computing systems. In this paper, we investigate alternatives for the storage subsystem of a novel exascale-capable system with special emphasis on how allocation strategies would affect the overall performance. We consider several aspects of data-aware allocation such as the effect of spatial and temporal locality, the affinity of data to storage sources, and the network-level traffic prioritization for different types of flows. In our experimental set-up, temporal locality can have a substantial effect on application runtime (up to a 10% reduction), whereas spatial locality can be even more significant (up to one order of magnitude faster with perfect locality). The use of structured access patterns to the data and the allocation of bandwidth at the network level can also have a significant impact (up to 20% and 17% reduction of runtime, respectively). These results suggest that scheduling policies exposing data-locality information can be essential for the appropriate utilization of future large-scale systems. Finally, we found that the distributed storage system we are implementing can outperform traditional SAN architectures, even with a much smaller (in terms of I/O servers) back-end.},\n bibtype = {article},\n author = {Pascual, Jose A and Lant, Joshua and Concatto, Caroline and Attwood, Andrew and Navaridas, Javier and Luján, Mikel and Goodacre, John},\n journal = {Concurrency and Computation: Practice and Experience},\n number = {21}\n}
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\n Summary The convergence between computing- and data-centric workloads and platforms is imposing new challenges on how to best use the resources of modern computing systems. In this paper, we investigate alternatives for the storage subsystem of a novel exascale-capable system with special emphasis on how allocation strategies would affect the overall performance. We consider several aspects of data-aware allocation such as the effect of spatial and temporal locality, the affinity of data to storage sources, and the network-level traffic prioritization for different types of flows. In our experimental set-up, temporal locality can have a substantial effect on application runtime (up to a 10% reduction), whereas spatial locality can be even more significant (up to one order of magnitude faster with perfect locality). The use of structured access patterns to the data and the allocation of bandwidth at the network level can also have a significant impact (up to 20% and 17% reduction of runtime, respectively). These results suggest that scheduling policies exposing data-locality information can be essential for the appropriate utilization of future large-scale systems. Finally, we found that the distributed storage system we are implementing can outperform traditional SAN architectures, even with a much smaller (in terms of I/O servers) back-end.\n
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\n \n\n \n \n \n \n \n Poster: An Exploratory Analysis of the Influence of the Temporal Information in Time Series Classification.\n \n \n \n\n\n \n Abanda, A.; Mori, U.; and Lozano, J., A.\n\n\n \n\n\n\n In FDST2019: Learning from Data Streams and Time Series: Convergences, Specificities and Common Challenges, 2019. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{\n title = {Poster: An Exploratory Analysis of the Influence of the Temporal Information in Time Series Classification},\n type = {inproceedings},\n year = {2019},\n id = {f3f8a6ea-ccb1-381a-a9b6-5971da603ed9},\n created = {2021-11-12T08:30:12.901Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:12.901Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Abanda, Amaia and Mori, Usue and Lozano, Jose Antonio},\n booktitle = {FDST2019: Learning from Data Streams and Time Series: Convergences, Specificities and Common Challenges}\n}
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\n \n\n \n \n \n \n \n Modeling and analysis of the performance of exascale photonic networks.\n \n \n \n\n\n \n Duro, J.; Pascual, J., A.; Petit, S.; Sahuquillo, J.; and Gómez, M., E.\n\n\n \n\n\n\n Concurrency and Computation: Practice and Experience, 31(21): e4773. 2019.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Modeling and analysis of the performance of exascale photonic networks},\n type = {article},\n year = {2019},\n keywords = {interconnection networks,photonic technology,simulation framework},\n pages = {e4773},\n volume = {31},\n id = {5ac5c60d-1621-3cd0-8ef3-b5f6c546550d},\n created = {2021-11-12T08:30:17.326Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:17.326Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {Summary Photonics technology has become a promising and viable alternative for both on-chip and off-chip interconnection networks of future Exascale systems. Nevertheless, this technology is not mature enough yet in this context, so research efforts focusing on photonic networks are still required to achieve realistic suitable network implementations. In this regard, system-level photonic network simulators can help guide designers to assess the multiple design choices. Most current research is done on electrical network simulators, whose components work widely different from photonics components. In this work, we summarize and compare the working behavior of both technologies which includes the use of optical routers, wavelength-division multiplexing and circuit switching among others. After implementing them into a well-known simulation framework, an extensive simulation study has been carried out using realistic photonic network configurations with synthetic and realistic traffic. Experimental results show that, compared to electrical networks, optical networks can reduce the execution time of the studied real workloads in almost one order of magnitude. Our study also reveals that the photonic configuration highly impacts on the network performance, being the bandwidth per channel and the message length the most important parameters.},\n bibtype = {article},\n author = {Duro, José and Pascual, Jose A and Petit, Salvador and Sahuquillo, Julio and Gómez, María E},\n journal = {Concurrency and Computation: Practice and Experience},\n number = {21}\n}
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\n Summary Photonics technology has become a promising and viable alternative for both on-chip and off-chip interconnection networks of future Exascale systems. Nevertheless, this technology is not mature enough yet in this context, so research efforts focusing on photonic networks are still required to achieve realistic suitable network implementations. In this regard, system-level photonic network simulators can help guide designers to assess the multiple design choices. Most current research is done on electrical network simulators, whose components work widely different from photonics components. In this work, we summarize and compare the working behavior of both technologies which includes the use of optical routers, wavelength-division multiplexing and circuit switching among others. After implementing them into a well-known simulation framework, an extensive simulation study has been carried out using realistic photonic network configurations with synthetic and realistic traffic. Experimental results show that, compared to electrical networks, optical networks can reduce the execution time of the studied real workloads in almost one order of magnitude. Our study also reveals that the photonic configuration highly impacts on the network performance, being the bandwidth per channel and the message length the most important parameters.\n
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\n \n\n \n \n \n \n \n Poster: Feature Extraction for Algorithm-Type Selection in Time Series Classification.\n \n \n \n\n\n \n Abanda, A.; Mori, U.; and Lozano, J., A.\n\n\n \n\n\n\n In 7th EITIC Doctoral Student Workshop, 2019. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Poster: Feature Extraction for Algorithm-Type Selection in Time Series Classification},\n type = {inproceedings},\n year = {2019},\n id = {0af2dbcd-0305-30ca-9a77-52d6693c3a59},\n created = {2021-11-12T08:30:19.292Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:19.292Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Abanda, Amaia and Mori, Usue and Lozano, Jose Antonio},\n booktitle = {7th EITIC Doctoral Student Workshop}\n}
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\n \n\n \n \n \n \n \n Data generation approaches for topic classification in multilingual spoken dialog system.\n \n \n \n\n\n \n Montenegro, C.; Santana, R.; and Lozano, J., A.\n\n\n \n\n\n\n In 12th Conference on PErvasive Technologies Related to Assistive Environments Conference (PETRA-19), 2019. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{\n title = {Data generation approaches for topic classification in multilingual spoken dialog system},\n type = {inproceedings},\n year = {2019},\n id = {08b0fffc-9557-34ea-895a-8dffe8c0ecc1},\n created = {2021-11-12T08:30:20.096Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:20.096Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Montenegro, C and Santana, R and Lozano, J A},\n booktitle = {12th Conference on PErvasive Technologies Related to Assistive Environments Conference (PETRA-19)}\n}
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\n \n\n \n \n \n \n \n A review on distance based time series classification.\n \n \n \n\n\n \n Abanda, A.; Mori, U.; and Lozano, J., A.\n\n\n \n\n\n\n Data Mining and Knowledge Discovery, 33(2): 378-412. 5 2019.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{\n title = {A review on distance based time series classification},\n type = {article},\n year = {2019},\n pages = {378-412},\n volume = {33},\n month = {5},\n id = {538d458c-5115-3d32-adb9-b5833f056e71},\n created = {2021-11-12T08:30:23.696Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:23.696Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Abanda, Amaia and Mori, Usue and Lozano, Jose A},\n journal = {Data Mining and Knowledge Discovery},\n number = {2}\n}
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\n \n\n \n \n \n \n \n Detection of sand dunes on Mars using a regular vine-based classification approach.\n \n \n \n\n\n \n Carrera, D.; Bandeira, L.; Santana, R.; and Lozano, J., A.\n\n\n \n\n\n\n Knowledge-Based Systems, 163: 858-874. 2019.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{\n title = {Detection of sand dunes on Mars using a regular vine-based classification approach},\n type = {article},\n year = {2019},\n pages = {858-874},\n volume = {163},\n publisher = {Elsevier},\n id = {4c64cfcb-8864-385b-b4bd-8f60edb186f3},\n created = {2021-11-12T08:30:25.107Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:25.107Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Carrera, Diana and Bandeira, Lourenço and Santana, Roberto and Lozano, José A},\n journal = {Knowledge-Based Systems}\n}
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\n \n\n \n \n \n \n \n Supervised non-parametric discretization based on Kernel density estimation.\n \n \n \n\n\n \n Flores, J., L.; Calvo, B.; and Perez, A.\n\n\n \n\n\n\n Pattern Recognition Letters. 2019.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Supervised non-parametric discretization based on Kernel density estimation},\n type = {article},\n year = {2019},\n keywords = {Discretization,Kernel density,Non-Parametric,Supervised},\n id = {314783d4-5b84-349f-a026-1bf43f708c3c},\n created = {2021-11-12T08:30:26.515Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:26.515Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Flores, Jose Luis and Calvo, Borja and Perez, Aritz},\n journal = {Pattern Recognition Letters}\n}
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\n \n\n \n \n \n \n \n A Note on the Behavior of Majority Voting in Multi-Class Domains with Biased Annotators.\n \n \n \n\n\n \n Hernández-González, J.; Inza, I.; and Lozano, J., A.\n\n\n \n\n\n\n IEEE Transactions on Knowledge and Data Engineering, 31(1): 195-200. 5 2019.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@article{\n title = {A Note on the Behavior of Majority Voting in Multi-Class Domains with Biased Annotators},\n type = {article},\n year = {2019},\n keywords = {Training;Labeling;Robustness;Standards;Noise measu},\n pages = {195-200},\n volume = {31},\n month = {5},\n id = {cfd6efa8-d168-3d16-8b39-ca42622c6eb5},\n created = {2021-11-12T08:30:32.018Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:32.018Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Hernández-González, J and Inza, I and Lozano, J A},\n journal = {IEEE Transactions on Knowledge and Data Engineering},\n number = {1}\n}
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\n \n\n \n \n \n \n \n Bayesian Performance Analysis for Black-Box Optimization Benchmarking.\n \n \n \n\n\n \n Calvo, B.; Shir, O., M.; Ceberio, J.; Doerr, C.; Back, T.; and Lozano, J., A.\n\n\n \n\n\n\n In Companion of the 2019 Genetic and Evolutionary Computation Conference (GECCO-2019). Workshop on Black Box Discrete Optimization Benchmarking., pages 1789-1797, 2019. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Bayesian Performance Analysis for Black-Box Optimization Benchmarking},\n type = {inproceedings},\n year = {2019},\n pages = {1789-1797},\n id = {7d690ef9-274e-387f-88b9-bd9e64120ad7},\n created = {2021-11-12T08:30:33.929Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:33.929Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Calvo, Borja and Shir, Offer M and Ceberio, Josu and Doerr, Carola and Back, Thomas and Lozano, Jose A},\n booktitle = {Companion of the 2019 Genetic and Evolutionary Computation Conference (GECCO-2019). Workshop on Black Box Discrete Optimization Benchmarking.}\n}
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\n \n\n \n \n \n \n \n Approaching the Quadratic Assignment Problem with Kernels of Mallows Models under the Hamming Distance.\n \n \n \n\n\n \n Arza, E.; Ceberio, J.; Irurozki, E.; and Perez, A.\n\n\n \n\n\n\n In Proceedings of the 2019 Genetic and Evolutionary Computation Conference (GECCO-2019), pages 141-142, 2019. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Approaching the Quadratic Assignment Problem with Kernels of Mallows Models under the Hamming Distance},\n type = {inproceedings},\n year = {2019},\n pages = {141-142},\n id = {a0fb592a-c9be-3a4c-9692-b047880a9dbb},\n created = {2021-11-12T08:30:35.354Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:35.354Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Arza, Etor and Ceberio, Josu and Irurozki, Ekhine and Perez, Aritz},\n booktitle = {Proceedings of the 2019 Genetic and Evolutionary Computation Conference (GECCO-2019)}\n}
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\n \n\n \n \n \n \n \n Vine copula classifiers for the mind reading problem.\n \n \n \n\n\n \n Carrera, D.; Santana, R.; and Lozano, J., A.\n\n\n \n\n\n\n Knowledge Based Systems, 163: 858-874. 2019.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{\n title = {Vine copula classifiers for the mind reading problem},\n type = {article},\n year = {2019},\n pages = {858-874},\n volume = {163},\n id = {84c6679b-da88-3474-93b8-c8f95de58db8},\n created = {2021-11-12T08:30:42.236Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:42.236Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Carrera, D and Santana, R and Lozano, J A},\n journal = {Knowledge Based Systems}\n}
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\n \n\n \n \n \n \n \n Using pairwise precedences for solving the linear ordering problem.\n \n \n \n\n\n \n Santucci, V.; and Ceberio, J.\n\n\n \n\n\n\n Applied Soft Computing,105998. 2019.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Using pairwise precedences for solving the linear ordering problem},\n type = {article},\n year = {2019},\n keywords = {Construction heuristic,Insert neighbourhood,Linear ordering problem,Meta-heuristic,Precedence,Variable neighborhood search},\n pages = {105998},\n id = {70df3dcd-4ac6-3c83-9410-6a5ac028ee32},\n created = {2021-11-12T08:30:43.694Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:43.694Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {It is an old claim that, in order to design a (meta)heuristic algorithm for solving a given optimization problem, algorithm designers need first to gain a deep insight into the structure of the problem. Nevertheless, in recent years, we have seen an incredible rise of “new” meta-heuristic paradigms that have been applied to any type of optimization problem without even considering the features of these problems. In this work, we put this initial claim into practice and try to solve a classical permutation problem: the Linear Ordering Problem (LOP). To that end, first, we study the structure of the LOP by focusing on the relation between the pairwise precedences of items in the solution and its objective value. In a second step, we design a new meta-heuristic scheme, namely CD-RVNS, that incorporates critical information about the problem in its three key algorithmic components: a variable neighborhood search algorithm, a construction heuristic, and a destruction procedure. Conducted experiments, on the most challenging LOP instances available in the literature, reveal an outstanding performance when compared to existing algorithms. Moreover, we also demonstrate (experimentally) that the developed heuristic procedures perform individually better than their state-of-the-art counterparts.},\n bibtype = {article},\n author = {Santucci, Valentino and Ceberio, Josu},\n journal = {Applied Soft Computing}\n}
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\n It is an old claim that, in order to design a (meta)heuristic algorithm for solving a given optimization problem, algorithm designers need first to gain a deep insight into the structure of the problem. Nevertheless, in recent years, we have seen an incredible rise of “new” meta-heuristic paradigms that have been applied to any type of optimization problem without even considering the features of these problems. In this work, we put this initial claim into practice and try to solve a classical permutation problem: the Linear Ordering Problem (LOP). To that end, first, we study the structure of the LOP by focusing on the relation between the pairwise precedences of items in the solution and its objective value. In a second step, we design a new meta-heuristic scheme, namely CD-RVNS, that incorporates critical information about the problem in its three key algorithmic components: a variable neighborhood search algorithm, a construction heuristic, and a destruction procedure. Conducted experiments, on the most challenging LOP instances available in the literature, reveal an outstanding performance when compared to existing algorithms. Moreover, we also demonstrate (experimentally) that the developed heuristic procedures perform individually better than their state-of-the-art counterparts.\n
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\n \n\n \n \n \n \n \n The EMPATHIC Project: Mid-term Achievements.\n \n \n \n\n\n \n Torres, M., L.; Olaso, J., M.; Montenegro, C.; Santana, R.; Vazquez, A.; Justo, R.; Lozano, J., A.; Schloegl, S.; Chollet, G.; Dugan, N.; Irvine, M.; Glackin, N.; Pickard, C.; Esposito, A.; Cordasco, G.; Troncone, A.; Petrovska-Delacretaz, D.; Mtibaa, A.; Hmani, M., A.; Korsnes, M., S.; Martinussen, L., J.; Escalera, S.; Palmero-Cantarino, C.; Deroo, O.; Gordeeva, O.; Tenerio-Laranga, J.; Gonzalez-Fraile, E.; Fernandez-Ruanova, B.; and Gonzalez-Pinto, A.\n\n\n \n\n\n\n In 12th Conference on PErvasive Technologies Related to Assistive Environments Conference (PETRA-19), 2019. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{\n title = {The EMPATHIC Project: Mid-term Achievements},\n type = {inproceedings},\n year = {2019},\n id = {c5dfb82e-a156-3925-a7d3-9fd152a68a8a},\n created = {2021-11-12T08:30:51.230Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:51.230Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Torres, M L and Olaso, J M and Montenegro, C and Santana, R and Vazquez, A and Justo, R and Lozano, J A and Schloegl, S and Chollet, G and Dugan, N and Irvine, M and Glackin, N and Pickard, C and Esposito, A and Cordasco, G and Troncone, A and Petrovska-Delacretaz, D and Mtibaa, A and Hmani, M A and Korsnes, M S and Martinussen, L J and Escalera, S and Palmero-Cantarino, C and Deroo, O and Gordeeva, O and Tenerio-Laranga, J and Gonzalez-Fraile, E and Fernandez-Ruanova, B and Gonzalez-Pinto, A},\n booktitle = {12th Conference on PErvasive Technologies Related to Assistive Environments Conference (PETRA-19)}\n}
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\n \n\n \n \n \n \n \n A mathematical analysis of EDAs with distance-based exponential models.\n \n \n \n\n\n \n Unanue, I.; Merino, M.; and Lozano, J., A.\n\n\n \n\n\n\n In Proceedings of the 2019 Genetic and Evolutionary Computation Conference (GECCO-2019), pages 429-430, 2019. \n \n\n\n\n
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@inproceedings{\n title = {A mathematical analysis of EDAs with distance-based exponential models},\n type = {inproceedings},\n year = {2019},\n pages = {429-430},\n id = {bce3176d-0d84-3497-b4c6-c9fc65defa1d},\n created = {2021-11-12T08:30:51.768Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:51.768Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Unanue, Imanol and Merino, Maria and Lozano, Jose A},\n booktitle = {Proceedings of the 2019 Genetic and Evolutionary Computation Conference (GECCO-2019)}\n}
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\n \n\n \n \n \n \n \n Adaptation of a Branching Algorithm to Solve Discrete Optimization Problems.\n \n \n \n\n\n \n Murua, M.; Galar, D.; and Santana, R.\n\n\n \n\n\n\n In Operations Research 2019, 2019. \n \n\n\n\n
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@inproceedings{\n title = {Adaptation of a Branching Algorithm to Solve Discrete Optimization Problems},\n type = {inproceedings},\n year = {2019},\n id = {4d89b19f-ae75-3df3-ae18-031d1aaa74fa},\n created = {2021-11-12T08:30:54.255Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:54.255Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Murua, Maialen and Galar, Diego and Santana, Roberto},\n booktitle = {Operations Research 2019}\n}
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\n \n\n \n \n \n \n \n Aggregated outputs by linear models: An application on marine litter beaching prediction.\n \n \n \n\n\n \n Hernández-González, J.; Inza, I.; Granado, I.; Basurko, O., C.; Fernandes, J., A.; and Lozano, J., A.\n\n\n \n\n\n\n Information Sciences, 481: 381-393. 2019.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Aggregated outputs by linear models: An application on marine litter beaching prediction},\n type = {article},\n year = {2019},\n keywords = {Aggregated outputs,Expectation–Maximization,Linear models,Machine learning,Marine litter beaching,Regression},\n pages = {381-393},\n volume = {481},\n id = {cc28644d-9826-368f-93dc-a69861cfed15},\n created = {2021-11-12T08:30:57.939Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:57.939Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {In regression, a predictive model which is able to anticipate the output of a new case is learnt from a set of previous examples. The output or response value of these examples used for model training is known. When learning with aggregated outputs, the examples available for model training are individually unlabeled. Collectively, the aggregated outputs of different subsets of training examples are provided. In this paper, we propose an iterative methodology to learn linear models from this type of data. In spite of being simple, its competitive performance is shown in comparison with a straightforward solution and state-of-the-art techniques. A real world problem is also illustrated which naturally fits the aggregated outputs framework: the estimation of marine litter beaching along the south-east coastline of the Bay of Biscay.},\n bibtype = {article},\n author = {Hernández-González, Jerónimo and Inza, Iñaki and Granado, Igor and Basurko, Oihane C and Fernandes, Jose A and Lozano, Jose A},\n journal = {Information Sciences}\n}
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\n In regression, a predictive model which is able to anticipate the output of a new case is learnt from a set of previous examples. The output or response value of these examples used for model training is known. When learning with aggregated outputs, the examples available for model training are individually unlabeled. Collectively, the aggregated outputs of different subsets of training examples are provided. In this paper, we propose an iterative methodology to learn linear models from this type of data. In spite of being simple, its competitive performance is shown in comparison with a straightforward solution and state-of-the-art techniques. A real world problem is also illustrated which naturally fits the aggregated outputs framework: the estimation of marine litter beaching along the south-east coastline of the Bay of Biscay.\n
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\n \n\n \n \n \n \n \n Optimal multi-impulse space rendezvous considering limited impulse using a discretized Lambert problem combined with evolutionary algorithms.\n \n \n \n\n\n \n Shirazi, A.; Ceberio, J.; and Lozano, J., A.\n\n\n \n\n\n\n In 8th European Conference for Aeronautics and Space Sciences (EUCASS), Madrid, Spain, 2019. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Optimal multi-impulse space rendezvous considering limited impulse using a discretized Lambert problem combined with evolutionary algorithms},\n type = {inproceedings},\n year = {2019},\n id = {c95c4b16-835c-3dae-b588-af61c5272889},\n created = {2021-11-12T08:30:59.099Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:59.099Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Shirazi, Abolfazl and Ceberio, Josu and Lozano, Jose A},\n booktitle = {8th European Conference for Aeronautics and Space Sciences (EUCASS), Madrid, Spain}\n}
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\n \n\n \n \n \n \n \n On-line Elastic Similarity Measures for time series.\n \n \n \n\n\n \n Oregi, I.; Pérez, A.; Ser, J., D.; and Lozano, J., A.\n\n\n \n\n\n\n Pattern Recognition, 88: 506-517. 2019.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {On-line Elastic Similarity Measures for time series},\n type = {article},\n year = {2019},\n keywords = {Dynamic time warping,Elastic similarity measures,Streaming data,Time series},\n pages = {506-517},\n volume = {88},\n id = {a0db5d34-9678-32c7-9b34-837c7168ac98},\n created = {2021-11-12T08:31:05.429Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:05.429Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {The way similarity is measured among time series is of paramount importance in many data mining and machine learning tasks. For instance, Elastic Similarity Measures are widely used to determine whether two time series are similar to each other. Indeed, in off-line time series mining, these measures have been shown to be very effective due to their ability to handle time distortions and mitigate their effect on the resulting distance. In the on-line setting, where available data increase continuously over time and not necessary in a stationary manner, stream mining approaches are required to be fast with limited memory consumption and capable of adapting to different stationary intervals. In this sense, the computational complexity of Elastic Similarity Measures and their lack of flexibility to accommodate different stationary intervals, make these similarity measures incompatible with the requirements mentioned. To overcome these issues, this paper adapts the family of Elastic Similarity Measures – which includes Dynamic Time Warping, Edit Distance, Edit Distance for Real Sequences and Edit Distance with Real Penalty – to the on-line setting. The proposed adaptation is based on two main ideas: a forgetting mechanism and the incremental computation. The former makes the similarity consistent with streaming time series characteristics by giving more importance to recent observations, whereas the latter reduces the computational complexity by avoiding unnecessary computations. In order to assess the behavior of the proposed similarity measure in on-line settings, two different experiments have been carried out. The first aims at showing the efficiency of the proposed adaptation, to do so we calculate and compare the computation time for the elastic measures and their on-line adaptation. By analyzing the results drawn from a distance-based streaming machine learning model, the second experiment intends to show the effect of the forgetting mechanism on the resulting similarity value. The experimentation shows, for the aforementioned Elastic Similarity Measures, that the proposed adaptation meets the memory, computational complexity and flexibility constraints imposed by streaming data.},\n bibtype = {article},\n author = {Oregi, Izaskun and Pérez, Aritz and Ser, Javier Del and Lozano, Jose A},\n journal = {Pattern Recognition}\n}
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\n\n\n
\n The way similarity is measured among time series is of paramount importance in many data mining and machine learning tasks. For instance, Elastic Similarity Measures are widely used to determine whether two time series are similar to each other. Indeed, in off-line time series mining, these measures have been shown to be very effective due to their ability to handle time distortions and mitigate their effect on the resulting distance. In the on-line setting, where available data increase continuously over time and not necessary in a stationary manner, stream mining approaches are required to be fast with limited memory consumption and capable of adapting to different stationary intervals. In this sense, the computational complexity of Elastic Similarity Measures and their lack of flexibility to accommodate different stationary intervals, make these similarity measures incompatible with the requirements mentioned. To overcome these issues, this paper adapts the family of Elastic Similarity Measures – which includes Dynamic Time Warping, Edit Distance, Edit Distance for Real Sequences and Edit Distance with Real Penalty – to the on-line setting. The proposed adaptation is based on two main ideas: a forgetting mechanism and the incremental computation. The former makes the similarity consistent with streaming time series characteristics by giving more importance to recent observations, whereas the latter reduces the computational complexity by avoiding unnecessary computations. In order to assess the behavior of the proposed similarity measure in on-line settings, two different experiments have been carried out. The first aims at showing the efficiency of the proposed adaptation, to do so we calculate and compare the computation time for the elastic measures and their on-line adaptation. By analyzing the results drawn from a distance-based streaming machine learning model, the second experiment intends to show the effect of the forgetting mechanism on the resulting similarity value. The experimentation shows, for the aforementioned Elastic Similarity Measures, that the proposed adaptation meets the memory, computational complexity and flexibility constraints imposed by streaming data.\n
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\n \n\n \n \n \n \n \n GP-based methods for domain adaptation: Using brain decoding across subjects as a test-case.\n \n \n \n\n\n \n Santana, R.; Marti, L.; and Zhang, M.\n\n\n \n\n\n\n Genetic Programming and Evolvable Machines, 20(3): 385-411. 2019.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {GP-based methods for domain adaptation: Using brain decoding across subjects as a test-case},\n type = {article},\n year = {2019},\n pages = {385-411},\n volume = {20},\n id = {955d4c65-85f8-3e4c-bff7-ea9f7f0a2eaa},\n created = {2021-11-12T08:31:06.780Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:06.780Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Santana, R and Marti, L and Zhang, M},\n journal = {Genetic Programming and Evolvable Machines},\n number = {3}\n}
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\n \n\n \n \n \n \n \n \n Analyzing rare event, anomaly, novelty and outlier detection terms under the supervised classification framework.\n \n \n \n \n\n\n \n Carreno, A.; Inza, I.; and Lozano, J., A.\n\n\n \n\n\n\n Artificial Intelligence Review. 10 2019.\n \n\n\n\n
\n\n\n\n \n \n \"AnalyzingWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Analyzing rare event, anomaly, novelty and outlier detection terms under the supervised classification framework},\n type = {article},\n year = {2019},\n websites = {https://doi.org/10.1007/s10462-019-09771-y},\n month = {10},\n id = {53d460d5-eeea-39de-8325-46acd5b9e7b3},\n created = {2021-11-12T08:31:08.440Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:08.440Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {In recent years, a variety of research areas have contributed to a set of related problems with rare event, anomaly, novelty and outlier detection terms as the main actors. These multiple research areas have created a mix-up between terminology and problems. In some research, similar problems have been named differently; while in some other works, the same term has been used to describe different problems. This confusion between terms and problems causes the repetition of research and hinders the advance of the field. Therefore, a standardization is imperative. The goal of this paper is to underline the differences between each term, and organize the area by looking at all these terms under the umbrella of supervised classification. Therefore, a one-to-one assignment of terms to learning scenarios is proposed. In fact, each learning scenario is associated with the term most frequently used in the literature. In order to validate this proposal, a set of experiments retrieving papers from Google Scholar, ACM Digital Library and IEEE Xplore has been carried out.},\n bibtype = {article},\n author = {Carreno, Ander and Inza, Iñaki and Lozano, Jose A},\n doi = {10.1007/s10462-019-09771-y},\n journal = {Artificial Intelligence Review}\n}
\n
\n\n\n
\n In recent years, a variety of research areas have contributed to a set of related problems with rare event, anomaly, novelty and outlier detection terms as the main actors. These multiple research areas have created a mix-up between terminology and problems. In some research, similar problems have been named differently; while in some other works, the same term has been used to describe different problems. This confusion between terms and problems causes the repetition of research and hinders the advance of the field. Therefore, a standardization is imperative. The goal of this paper is to underline the differences between each term, and organize the area by looking at all these terms under the umbrella of supervised classification. Therefore, a one-to-one assignment of terms to learning scenarios is proposed. In fact, each learning scenario is associated with the term most frequently used in the literature. In order to validate this proposal, a set of experiments retrieving papers from Google Scholar, ACM Digital Library and IEEE Xplore has been carried out.\n
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\n \n\n \n \n \n \n \n Sentiment analysis with genetically evolved Gaussian kernels.\n \n \n \n\n\n \n Roman, I.; Santana, R.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n In Proceedings of the 2019 on Genetic and Evolutionary Computation Conference, 2019. ACM\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Sentiment analysis with genetically evolved Gaussian kernels},\n type = {inproceedings},\n year = {2019},\n publisher = {ACM},\n id = {08bb4613-9d81-376b-a461-41328c5f5c0a},\n created = {2021-11-12T08:31:14.346Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:14.346Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Roman, Ibai and Santana, Roberto and Mendiburu, Alexander and Lozano, Jose Antonio},\n booktitle = {Proceedings of the 2019 on Genetic and Evolutionary Computation Conference}\n}
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\n \n\n \n \n \n \n \n Enabling shared memory communication in networks of MPSoCs.\n \n \n \n\n\n \n Lant, J.; Concatto, C.; Attwood, A.; Pascual, J., A.; Ashworth, M.; Navaridas, J.; Luján, M.; and Goodacre, J.\n\n\n \n\n\n\n Concurrency and Computation: Practice and Experience, 31(21): e4774. 2019.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Enabling shared memory communication in networks of MPSoCs},\n type = {article},\n year = {2019},\n keywords = {FPGA,HPC,MPSoC,distributed shared memory,interconnect,networks},\n pages = {e4774},\n volume = {31},\n id = {fd99d9b0-60c0-33f1-b6ea-546a91ef0dfd},\n created = {2021-11-12T08:31:15.248Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:15.248Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {Summary Ongoing transistor scaling and the growing complexity of embedded system designs has led to the rise of MPSoCs (Multi-Processor System-on-Chip), combining multiple hard-core CPUs and accelerators (FPGA, GPU) on the same physical die. These devices are of great interest to the supercomputing community, who are increasingly reliant on heterogeneity to achieve power and performance goals in these closing stages of the race to exascale. In this paper, we present a network interface architecture and networking infrastructure, designed to sit inside the FPGA fabric of a cutting-edge MPSoC device, enabling networks of these devices to communicate within both a distributed and shared memory context, with reduced need for costly software networking system calls. We will present our implementation and prototype system and discuss the main design decisions relevant to the use of the Xilinx Zynq Ultrascale+, a state-of-the-art MPSoC, and the challenges to be overcome given the device's limitations and constraints. We demonstrate the working prototype system connecting two MPSoCs, with communication between processor and remote memory region and accelerator. We then discuss the limitations of the current implementation and highlight areas of improvement to make this solution production-ready.},\n bibtype = {article},\n author = {Lant, Joshua and Concatto, Caroline and Attwood, Andrew and Pascual, Jose A and Ashworth, Mike and Navaridas, Javier and Luján, Mikel and Goodacre, John},\n journal = {Concurrency and Computation: Practice and Experience},\n number = {21}\n}
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\n Summary Ongoing transistor scaling and the growing complexity of embedded system designs has led to the rise of MPSoCs (Multi-Processor System-on-Chip), combining multiple hard-core CPUs and accelerators (FPGA, GPU) on the same physical die. These devices are of great interest to the supercomputing community, who are increasingly reliant on heterogeneity to achieve power and performance goals in these closing stages of the race to exascale. In this paper, we present a network interface architecture and networking infrastructure, designed to sit inside the FPGA fabric of a cutting-edge MPSoC device, enabling networks of these devices to communicate within both a distributed and shared memory context, with reduced need for costly software networking system calls. We will present our implementation and prototype system and discuss the main design decisions relevant to the use of the Xilinx Zynq Ultrascale+, a state-of-the-art MPSoC, and the challenges to be overcome given the device's limitations and constraints. We demonstrate the working prototype system connecting two MPSoCs, with communication between processor and remote memory region and accelerator. We then discuss the limitations of the current implementation and highlight areas of improvement to make this solution production-ready.\n
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\n \n\n \n \n \n \n \n Anatomy of the Attraction Basins: Breaking with the Intuition.\n \n \n \n\n\n \n Hernando, L.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n Evolutionary Computation, 27(3): 435-466. 2019.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{\n title = {Anatomy of the Attraction Basins: Breaking with the Intuition},\n type = {article},\n year = {2019},\n pages = {435-466},\n volume = {27},\n id = {c6a6b44c-ab1b-31b4-aec8-76101e431059},\n created = {2021-11-12T08:31:15.502Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:15.502Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Hernando, Leticia and Mendiburu, Alexander and Lozano, Jose A},\n journal = {Evolutionary Computation},\n number = {3}\n}
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\n \n\n \n \n \n \n \n Penalized Partial Least Square applied to structured data.\n \n \n \n\n\n \n Broc, C.; Calvo, B.; and Liquet, B.\n\n\n \n\n\n\n Arabian Journal of Mathematics,1-16. 2019.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{\n title = {Penalized Partial Least Square applied to structured data},\n type = {article},\n year = {2019},\n pages = {1-16},\n publisher = {Springer},\n id = {210af11b-b264-362c-bf3c-baca34c2d851},\n created = {2021-11-12T08:31:15.756Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:15.756Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Broc, Camilo and Calvo, Borja and Liquet, Benoit},\n journal = {Arabian Journal of Mathematics}\n}
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\n \n\n \n \n \n \n \n On the evaluation and selection of classifier learning algorithms with crowdsourced data.\n \n \n \n\n\n \n Urkullu, A.; Pérez, A.; and Calvo, B.\n\n\n \n\n\n\n Applied Soft Computing. 2019.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{\n title = {On the evaluation and selection of classifier learning algorithms with crowdsourced data},\n type = {article},\n year = {2019},\n publisher = {Elsevier},\n id = {7c5b0d73-f6dc-3041-bc49-814403c17522},\n created = {2021-11-12T08:31:22.708Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:22.708Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Urkullu, A and Pérez, A and Calvo, B},\n journal = {Applied Soft Computing}\n}
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\n \n\n \n \n \n \n \n A dialogue-act taxonomy for a virtual coach designed to improve the life of elderly.\n \n \n \n\n\n \n Montenegro, C.; López Zorrilla, A.; Mikel Olaso, J.; Santana, R.; Justo, R.; Lozano, J., A.; and Torres, M., I.\n\n\n \n\n\n\n Multimodal Technologies and Interaction, 3(3): 52. 2019.\n \n\n\n\n
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@article{\n title = {A dialogue-act taxonomy for a virtual coach designed to improve the life of elderly},\n type = {article},\n year = {2019},\n pages = {52},\n volume = {3},\n publisher = {Multidisciplinary Digital Publishing Institute},\n id = {cce6d1f2-46ae-3876-a4b8-5355463ae586},\n created = {2021-11-12T08:31:24.093Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:24.093Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Montenegro, César and López Zorrilla, Asier and Mikel Olaso, Javier and Santana, Roberto and Justo, Raquel and Lozano, Jose A and Torres, Mar\\'\\ia Inés},\n journal = {Multimodal Technologies and Interaction},\n number = {3}\n}
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\n \n\n \n \n \n \n \n On the Definition of Dynamic Optimization Problems under the Rotation of the Landscape.\n \n \n \n\n\n \n Alza, J.; Barlett, M.; Ceberio, J.; and McCall, J.\n\n\n \n\n\n\n In Companion of the 2019 Genetic and Evolutionary Computation Conference (GECCO-2019). Workshop on Evolutionary Computation for Permutation Problems., pages 1518-1526, 2019. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {On the Definition of Dynamic Optimization Problems under the Rotation of the Landscape},\n type = {inproceedings},\n year = {2019},\n pages = {1518-1526},\n id = {6ef06e3c-ee9d-3f52-9797-e03416ab316e},\n created = {2021-11-12T08:31:32.207Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:32.207Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Alza, Joan and Barlett, Mark and Ceberio, Josu and McCall, John},\n booktitle = {Companion of the 2019 Genetic and Evolutionary Computation Conference (GECCO-2019). Workshop on Evolutionary Computation for Permutation Problems.}\n}
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\n \n\n \n \n \n \n \n Application of machine learning techniques to weather forecasting.\n \n \n \n\n\n \n Rozas, P.\n\n\n \n\n\n\n Ph.D. Thesis, 2019.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@phdthesis{\n title = {Application of machine learning techniques to weather forecasting},\n type = {phdthesis},\n year = {2019},\n institution = {University of the Basque Country (UPV/EHU)},\n id = {c4b678f9-3040-38ca-a754-a4e7c362f87f},\n created = {2021-11-12T08:31:36.739Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:36.739Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {phdthesis},\n private_publication = {false},\n bibtype = {phdthesis},\n author = {Rozas, Pablo}\n}
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\n \n\n \n \n \n \n \n An Experimental Study in Adaptive Kernel Selection for Bayesian Optimization.\n \n \n \n\n\n \n Roman, I.; Santana, R.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n IEEE Access, 7: 184294-184302. 2019.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {An Experimental Study in Adaptive Kernel Selection for Bayesian Optimization},\n type = {article},\n year = {2019},\n keywords = {Adaptive kernel selection,Bayesian optimization,Gaussian process,parameter tuning},\n pages = {184294-184302},\n volume = {7},\n id = {ab101179-f7b5-3454-997c-adc047cdf750},\n created = {2021-11-12T08:31:40.386Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:40.386Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {Bayesian Optimization has been widely used along with Gaussian Processes for solving expensive-to-evaluate black-box optimization problems. Overall, this approach has shown good results, and particularly for parameter tuning of machine learning algorithms. Nonetheless, Bayesian Optimization has to be also configured to achieve the best possible performance, being the selection of the kernel function a crucial choice. This paper investigates the convenience of adaptively changing the kernel function during the optimization process, instead of fixing it a priori. Six adaptive kernel selection strategies are introduced and tested in well-known synthetic and real-world optimization problems. In order to provide a more complete evaluation of the proposed kernel selection variants, two major kernel parameter setting approaches have been tested. According to our results, apart from having the advantage of removing the selection of the kernel out of the equation, adaptive kernel selection criteria show a better performance than fixed-kernel approaches.},\n bibtype = {article},\n author = {Roman, Ibai and Santana, Roberto and Mendiburu, Alexander and Lozano, Jose A},\n journal = {IEEE Access}\n}
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\n Bayesian Optimization has been widely used along with Gaussian Processes for solving expensive-to-evaluate black-box optimization problems. Overall, this approach has shown good results, and particularly for parameter tuning of machine learning algorithms. Nonetheless, Bayesian Optimization has to be also configured to achieve the best possible performance, being the selection of the kernel function a crucial choice. This paper investigates the convenience of adaptively changing the kernel function during the optimization process, instead of fixing it a priori. Six adaptive kernel selection strategies are introduced and tested in well-known synthetic and real-world optimization problems. In order to provide a more complete evaluation of the proposed kernel selection variants, two major kernel parameter setting approaches have been tested. According to our results, apart from having the advantage of removing the selection of the kernel out of the equation, adaptive kernel selection criteria show a better performance than fixed-kernel approaches.\n
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\n \n\n \n \n \n \n \n INRFlow: An interconnection networks research flow-level simulation framework.\n \n \n \n\n\n \n Navaridas, J.; Pascual, J., A.; Erickson, A.; Stewart, I., A.; and Luján, M.\n\n\n \n\n\n\n Journal of Parallel and Distributed Computing, 130: 140-152. 2019.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {INRFlow: An interconnection networks research flow-level simulation framework},\n type = {article},\n year = {2019},\n keywords = {Datacentres,Interconnection networks,Large-scale systems,Network topologies and routing,Simulation and modelling,Supercomputers},\n pages = {140-152},\n volume = {130},\n id = {cef25feb-69a5-3fd9-b8df-0da13a832bed},\n created = {2021-11-12T08:31:40.668Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:40.668Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {This paper presents INRFlow, a mature, frugal, flow-level simulation framework for modelling large-scale networks and computing systems. INRFlow is designed to carry out performance-related studies of interconnection networks for both high performance computing systems and datacentres. It features a completely modular design in which adding new topologies, routings or traffic models requires minimum effort. Moreover, INRFlow includes two different simulation engines: a static engine that is able to scale to tens of millions of nodes and a dynamic one that captures temporal and causal relationships to provide more realistic simulations. We will describe the main aspects of the simulator, including system models, traffic models and the large variety of topologies and routings implemented so far. We conclude the paper with a case study that analyses the scalability of several typical topologies. INRFlow has been used to conduct a variety of studies including evaluation of novel topologies and routings (both in the context of graph theory and optimization), analysis of storage and bandwidth allocation strategies and understanding of interferences between application and storage traffic.},\n bibtype = {article},\n author = {Navaridas, Javier and Pascual, Jose A and Erickson, Alejandro and Stewart, Iain A and Luján, Mikel},\n journal = {Journal of Parallel and Distributed Computing}\n}
\n
\n\n\n
\n This paper presents INRFlow, a mature, frugal, flow-level simulation framework for modelling large-scale networks and computing systems. INRFlow is designed to carry out performance-related studies of interconnection networks for both high performance computing systems and datacentres. It features a completely modular design in which adding new topologies, routings or traffic models requires minimum effort. Moreover, INRFlow includes two different simulation engines: a static engine that is able to scale to tens of millions of nodes and a dynamic one that captures temporal and causal relationships to provide more realistic simulations. We will describe the main aspects of the simulator, including system models, traffic models and the large variety of topologies and routings implemented so far. We conclude the paper with a case study that analyses the scalability of several typical topologies. INRFlow has been used to conduct a variety of studies including evaluation of novel topologies and routings (both in the context of graph theory and optimization), analysis of storage and bandwidth allocation strategies and understanding of interferences between application and storage traffic.\n
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\n \n\n \n \n \n \n \n A machine-learning based method for the refinement of variants in FFPE DNA sequencing data.\n \n \n \n\n\n \n Tellaetxe-Abete, M.; Lawrie, C.; and Calvo, B.\n\n\n \n\n\n\n In ISMB/ECCB 2019, 2019. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {A machine-learning based method for the refinement of variants in FFPE DNA sequencing data},\n type = {inproceedings},\n year = {2019},\n id = {5a253a0a-1535-3773-a66c-21f37b64934e},\n created = {2021-11-12T08:31:41.753Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:41.753Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Tellaetxe-Abete, Maitena and Lawrie, Charles and Calvo, Borja},\n booktitle = {ISMB/ECCB 2019}\n}
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\n \n\n \n \n \n \n \n Characterising the Rankings Produced by Combinatorial Optimisation Problems and Finding their Intersections.\n \n \n \n\n\n \n Hernando, L.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n In In Proceedings of The Genetic and Evolutionary Computation Conference, pages 266-273, 2019. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Characterising the Rankings Produced by Combinatorial Optimisation Problems and Finding their Intersections},\n type = {inproceedings},\n year = {2019},\n pages = {266-273},\n id = {9f76b0fa-ae8f-325a-8ac1-a0f065ae7a44},\n created = {2021-11-12T08:31:47.365Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:47.365Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Hernando, Leticia and Mendiburu, Alexander and Lozano, Jose A},\n booktitle = {In Proceedings of The Genetic and Evolutionary Computation Conference}\n}
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\n \n\n \n \n \n \n \n An evolutionary discretized Lambert approach for optimal long-range rendezvous considering impulse limit.\n \n \n \n\n\n \n Shirazi, A.; Ceberio, J.; and Lozano, J., A.\n\n\n \n\n\n\n Aerospace Science and Technology, 94. 2019.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {An evolutionary discretized Lambert approach for optimal long-range rendezvous considering impulse limit},\n type = {article},\n year = {2019},\n volume = {94},\n id = {66f8ff15-fc20-37d0-9210-1579f6646c95},\n created = {2021-11-12T08:31:50.432Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:50.432Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Shirazi, Abolfazl and Ceberio, Josu and Lozano, Jose A},\n journal = {Aerospace Science and Technology}\n}
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\n \n\n \n \n \n \n \n Early classification of time series using multi-objective optimization techniques.\n \n \n \n\n\n \n Mori, U.; Mendiburu, A.; Miranda, I., M.; and Lozano, J., A.\n\n\n \n\n\n\n Information Sciences, 492: 204-218. 2019.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Early classification of time series using multi-objective optimization techniques},\n type = {article},\n year = {2019},\n keywords = {Early classification,Multi-objective optimization,Time series classification},\n pages = {204-218},\n volume = {492},\n id = {f114f429-754d-3e88-9a10-c402fa14cabf},\n created = {2021-11-12T08:31:55.979Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:55.979Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Mori, Usue and Mendiburu, Alexander and Miranda, I M and Lozano, Jose A},\n journal = {Information Sciences}\n}
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\n \n\n \n \n \n \n \n Hybrid Heuristics for the Linear Ordering Problem.\n \n \n \n\n\n \n Erik Garcia Josu Ceberio, J., A., L.\n\n\n \n\n\n\n In 2019 IEEE Congress on Evolutionary Computation (CEC-2019), Wellington, New Zeland., pages 1431-1438, 2019. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Hybrid Heuristics for the Linear Ordering Problem},\n type = {inproceedings},\n year = {2019},\n pages = {1431-1438},\n id = {0e8680c8-6055-3485-9c38-eb6bc6e45978},\n created = {2021-11-12T08:31:56.249Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:56.249Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Erik Garcia Josu Ceberio, Jose A Lozano},\n booktitle = {2019 IEEE Congress on Evolutionary Computation (CEC-2019), Wellington, New Zeland.}\n}
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\n \n\n \n \n \n \n \n Evolving Neural Networks in Reinforcement Learning by means of UMDAc.\n \n \n \n\n\n \n Malagon, M.; and Ceberio, J.\n\n\n \n\n\n\n arXiv:1904.10932. 2019.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{\n title = {Evolving Neural Networks in Reinforcement Learning by means of UMDAc},\n type = {article},\n year = {2019},\n id = {eccb21e9-dd46-3831-8f38-86acfe011776},\n created = {2021-11-12T08:31:58.435Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:58.435Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Malagon, Mikel and Ceberio, Josu},\n journal = {arXiv:1904.10932}\n}
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\n \n\n \n \n \n \n \n Taxonomization of Combinatorial Optimization Problems in Fourier Space.\n \n \n \n\n\n \n Elorza, A.; Hernando, L.; and Lozano, J., A.\n\n\n \n\n\n\n arXiv preprint arXiv:1905.10852. 2019.\n \n\n\n\n
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@article{\n title = {Taxonomization of Combinatorial Optimization Problems in Fourier Space},\n type = {article},\n year = {2019},\n id = {177f975d-a42d-35f4-823e-6aba2a853d23},\n created = {2021-11-12T08:32:05.682Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:05.682Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Elorza, Anne and Hernando, Leticia and Lozano, Jose A},\n journal = {arXiv preprint arXiv:1905.10852}\n}
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\n \n\n \n \n \n \n \n Mallows and generalized Mallows model for matchings.\n \n \n \n\n\n \n Irurozki, E.; Calvo, B.; and Lozano, J., A.\n\n\n \n\n\n\n Bernoulli, 25(2): 1160-1188. 2019.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{\n title = {Mallows and generalized Mallows model for matchings},\n type = {article},\n year = {2019},\n pages = {1160-1188},\n volume = {25},\n publisher = {Bernoulli Society for Mathematical Statistics and Probability},\n id = {9c035a6e-69ed-3769-bd1e-d59bd76ba193},\n created = {2021-11-12T08:32:13.348Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:13.348Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Irurozki, Ekhine and Calvo, Borja and Lozano, Jose A},\n journal = {Bernoulli},\n number = {2}\n}
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\n \n\n \n \n \n \n \n Optimizing permutation-based problems with a discrete vine-copula as a model for eda.\n \n \n \n\n\n \n Cheriet, A.; and Santana, R.\n\n\n \n\n\n\n In Proceedings of the Genetic and Evolutionary Computation Conference Companion, pages 143-144, 2019. \n \n\n\n\n
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@inproceedings{\n title = {Optimizing permutation-based problems with a discrete vine-copula as a model for eda},\n type = {inproceedings},\n year = {2019},\n pages = {143-144},\n id = {7b1a7b7a-2a8c-33a5-9682-e2f49ccb4ae4},\n created = {2021-11-12T08:32:13.888Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:13.888Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Cheriet, Abdelhakim and Santana, Roberto},\n booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion}\n}
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\n \n\n \n \n \n \n \n Creating Difficult Instances of the Linear Ordering Problem.\n \n \n \n\n\n \n Perez, A.; and Ceberio, J.\n\n\n \n\n\n\n In XIII Congreso Español de Metaheuristicas, Algoritmos Evolutivos y Bioinspirados (MAEB 2018). Actas CAEPIA 2018, pages 733-738, 2018. \n \n\n\n\n
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@inproceedings{\n title = {Creating Difficult Instances of the Linear Ordering Problem},\n type = {inproceedings},\n year = {2018},\n pages = {733-738},\n id = {274a51f0-898c-33d9-90d4-fca1cb0af37a},\n created = {2021-11-12T08:30:02.435Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:02.435Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Perez, Aritz and Ceberio, Josu},\n booktitle = {XIII Congreso Español de Metaheuristicas, Algoritmos Evolutivos y Bioinspirados (MAEB 2018). Actas CAEPIA 2018}\n}
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\n \n\n \n \n \n \n \n Eventos raros, anomalías y novedades vistas desde el paraguas de la clasificación supervisada.\n \n \n \n\n\n \n Carreno, A.; Inza, I.; and Lozano, J., A.\n\n\n \n\n\n\n In IX Simposio Teoría y Aplicaciones de Minería de Datos, pages 925, 2018. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{\n title = {Eventos raros, anomalías y novedades vistas desde el paraguas de la clasificación supervisada},\n type = {inproceedings},\n year = {2018},\n pages = {925},\n id = {e12c8bf4-81c0-3779-8c4b-0ecaced62f5b},\n created = {2021-11-12T08:30:03.975Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:03.975Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {930},\n bibtype = {inproceedings},\n author = {Carreno, Ander and Inza, Inaki and Lozano, Jose A},\n booktitle = {IX Simposio Teoría y Aplicaciones de Minería de Datos}\n}
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\n \n\n \n \n \n \n \n Are the artificially generated instances uniform in terms of difficulty?.\n \n \n \n\n\n \n Aritz Perez Josu Ceberio, J., A., L.\n\n\n \n\n\n\n In 2018 IEEE Congress on Evolutionary Computation (CEC-2018), Rio de Janeiro, Brazil, 2018. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{\n title = {Are the artificially generated instances uniform in terms of difficulty?},\n type = {inproceedings},\n year = {2018},\n id = {1eb9d853-6ce3-306e-adf5-52a11fdc1195},\n created = {2021-11-12T08:30:04.811Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:04.811Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Aritz Perez Josu Ceberio, Jose A Lozano},\n booktitle = {2018 IEEE Congress on Evolutionary Computation (CEC-2018), Rio de Janeiro, Brazil}\n}
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\n \n\n \n \n \n \n \n A Decomposition-based Local Search Algorithm for Multi-objective Sequence Dependent Setup Times Permutation Flowshop Scheduling.\n \n \n \n\n\n \n Murilo Zangari Josu Ceberio, A., A., C.\n\n\n \n\n\n\n In 2018 IEEE Congress on Evolutionary Computation (CEC-2018), Rio de Janeiro, Brazil, 2018. \n \n\n\n\n
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@inproceedings{\n title = {A Decomposition-based Local Search Algorithm for Multi-objective Sequence Dependent Setup Times Permutation Flowshop Scheduling},\n type = {inproceedings},\n year = {2018},\n id = {ebc7562d-c891-39d3-ae9d-98126a46e425},\n created = {2021-11-12T08:30:05.077Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:05.077Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Murilo Zangari Josu Ceberio, Ademir A Constantino},\n booktitle = {2018 IEEE Congress on Evolutionary Computation (CEC-2018), Rio de Janeiro, Brazil}\n}
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\n \n\n \n \n \n \n \n The weighted independent domination problem: Integer linear programming models and metaheuristic approaches.\n \n \n \n\n\n \n Davidson, P., P.; Blum, C.; and Lozano, J., A.\n\n\n \n\n\n\n European Journal of Operational Research, 265(3): 860-871. 2018.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{\n title = {The weighted independent domination problem: Integer linear programming models and metaheuristic approaches},\n type = {article},\n year = {2018},\n pages = {860-871},\n volume = {265},\n id = {6a52f7bb-e7cc-3e8a-9717-7ea0ea50e307},\n created = {2021-11-12T08:30:11.204Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:11.204Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Davidson, Pedro Pinacho and Blum, Christian and Lozano, José Antonio},\n journal = {European Journal of Operational Research},\n number = {3}\n}
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\n \n\n \n \n \n \n \n On the Study of Crowdsourced Labeled Data and Annotators: Beyond Noisy Labels.\n \n \n \n\n\n \n Benaran-Munoz, I.\n\n\n \n\n\n\n In IX Simposio Teoría y Aplicaciones de Minería de Datos, 2018. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{\n title = {On the Study of Crowdsourced Labeled Data and Annotators: Beyond Noisy Labels},\n type = {inproceedings},\n year = {2018},\n id = {541ce697-11ac-3a0f-995b-77fe00efb622},\n created = {2021-11-12T08:30:13.162Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:13.162Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Benaran-Munoz, Iker},\n booktitle = {IX Simposio Teoría y Aplicaciones de Minería de Datos}\n}
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\n \n\n \n \n \n \n \n Estimating attraction basin sizes of combinatorial optimization problems.\n \n \n \n\n\n \n Elorza, A.; Hernando, L.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n Progress in Artificial Intelligence, 7(4): 369-384. 2018.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{\n title = {Estimating attraction basin sizes of combinatorial optimization problems},\n type = {article},\n year = {2018},\n pages = {369-384},\n volume = {7},\n publisher = {Springer},\n id = {6fd10d44-b941-3be2-9b02-507d9dcca65b},\n created = {2021-11-12T08:30:13.709Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:13.709Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Elorza, Anne and Hernando, Leticia and Mendiburu, Alexander and Lozano, Jose A},\n journal = {Progress in Artificial Intelligence},\n number = {4}\n}
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\n \n\n \n \n \n \n \n Hill-climbing algorithm: let's go for a walk before finding the optimum.\n \n \n \n\n\n \n Hernando, L.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n In Proceeding of 2018 IEEE Congress on Evolutionary Computation, pages 1292-1298, 2018. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{\n title = {Hill-climbing algorithm: let's go for a walk before finding the optimum},\n type = {inproceedings},\n year = {2018},\n pages = {1292-1298},\n id = {e83fcee5-21ef-311b-aad4-0580c3896855},\n created = {2021-11-12T08:30:22.620Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:22.620Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Hernando, Leticia and Mendiburu, Alexander and Lozano, Jose A},\n booktitle = {Proceeding of 2018 IEEE Congress on Evolutionary Computation}\n}
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\n \n\n \n \n \n \n \n High-Performance, Low-Complexity Deadlock Avoidance for Arbitrary Topologies/Routings.\n \n \n \n\n\n \n Pascual, J., A.; and Navaridas, J.\n\n\n \n\n\n\n In Proceedings of the 32nd International Conference on Supercomputing, ICS 2018, Beijing, China, June 12-15, 2018, pages 129-138, 2018. ACM\n \n\n\n\n
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@inproceedings{\n title = {High-Performance, Low-Complexity Deadlock Avoidance for Arbitrary Topologies/Routings},\n type = {inproceedings},\n year = {2018},\n pages = {129-138},\n publisher = {ACM},\n id = {be14614a-0ee7-3108-92be-12d87c4e0efa},\n created = {2021-11-12T08:30:27.571Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:27.571Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Pascual, Jose Antonio and Navaridas, Javier},\n booktitle = {Proceedings of the 32nd International Conference on Supercomputing, ICS 2018, Beijing, China, June 12-15, 2018}\n}
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\n \n\n \n \n \n \n \n Multi-Objectivising Combinatorial Optimisation Problems by Means of Elementary Landscape Decompositions.\n \n \n \n\n\n \n Ceberio, J.; Calvo, B.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n Evolutionary Computation, 0(0): 1-21. 2018.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{\n title = {Multi-Objectivising Combinatorial Optimisation Problems by Means of Elementary Landscape Decompositions},\n type = {article},\n year = {2018},\n pages = {1-21},\n volume = {0},\n id = {b3a96aea-e3c7-3e71-a4ed-86294f54b544},\n created = {2021-11-12T08:30:28.539Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:28.539Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Ceberio, Josu and Calvo, Borja and Mendiburu, Alexander and Lozano, Jose A},\n journal = {Evolutionary Computation},\n number = {0}\n}
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\n \n\n \n \n \n \n \n An efficient evolutionary algorithm for the orienteering problem.\n \n \n \n\n\n \n Kobeaga, G.; Merino, M.; and Lozano, J., A.\n\n\n \n\n\n\n Computers & OR, 90: 42-59. 2018.\n \n\n\n\n
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@article{\n title = {An efficient evolutionary algorithm for the orienteering problem},\n type = {article},\n year = {2018},\n pages = {42-59},\n volume = {90},\n id = {ed3bcf63-5049-3bc2-b345-653994d66d71},\n created = {2021-11-12T08:30:36.718Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:36.718Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Kobeaga, Gorka and Merino, Mar\\'\\ia and Lozano, José Antonio},\n journal = {Computers & OR}\n}
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\n \n\n \n \n \n \n \n Effects of Reducing VMs Management Times on Elastic Applications.\n \n \n \n\n\n \n Pascual, J., A.; Lozano, J., A.; and Miguel-Alonso, J.\n\n\n \n\n\n\n J. Grid Comput., 16(3): 513-530. 2018.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{\n title = {Effects of Reducing VMs Management Times on Elastic Applications},\n type = {article},\n year = {2018},\n pages = {513-530},\n volume = {16},\n id = {88641d82-40a2-3e19-86a9-e5070ae7519a},\n created = {2021-11-12T08:30:37.829Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:37.829Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Pascual, Jose Antonio and Lozano, José Antonio and Miguel-Alonso, José},\n journal = {J. Grid Comput.},\n number = {3}\n}
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\n \n\n \n \n \n \n \n Balancing the Diversification-Intensification Trade-off Using Mixtures of Probability Models.\n \n \n \n\n\n \n Joan Alza Josu Ceberio, B., C.\n\n\n \n\n\n\n In Are the artificially generated instances uniform in terms of difficulty?, 2018. \n \n\n\n\n
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@inproceedings{\n title = {Balancing the Diversification-Intensification Trade-off Using Mixtures of Probability Models},\n type = {inproceedings},\n year = {2018},\n id = {0845b2d7-2617-3d21-8bf5-a4374c874fb5},\n created = {2021-11-12T08:30:44.525Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:44.525Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Joan Alza Josu Ceberio, Borja Calvo},\n booktitle = {Are the artificially generated instances uniform in terms of difficulty?}\n}
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\n \n\n \n \n \n \n \n Theoretical and Methodological Advances in Semi-Supervided Learning and the Class-Imbalance Problem.\n \n \n \n\n\n \n Ortigosa-Hernández, J.\n\n\n \n\n\n\n Ph.D. Thesis, 2018.\n \n\n\n\n
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@phdthesis{\n title = {Theoretical and Methodological Advances in Semi-Supervided Learning and the Class-Imbalance Problem},\n type = {phdthesis},\n year = {2018},\n pages = {221},\n institution = {University of the Basque Country},\n id = {7ad43b5d-db52-3384-93bf-c401aad457dd},\n created = {2021-11-12T08:30:44.807Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:44.807Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {phdthesis},\n private_publication = {false},\n bibtype = {phdthesis},\n author = {Ortigosa-Hernández, Jonathan}\n}
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\n \n\n \n \n \n \n \n Evolved GANs for generating Pareto set approximations.\n \n \n \n\n\n \n Garciarena, U.; Santana, R.; and Mendiburu, A.\n\n\n \n\n\n\n In Proceedings of the Genetic and Evolutionary Computation Conference, pages 434-441, 2018. \n \n\n\n\n
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@inproceedings{\n title = {Evolved GANs for generating Pareto set approximations},\n type = {inproceedings},\n year = {2018},\n pages = {434-441},\n id = {15398a6f-3cc6-3368-9140-8f711924ec48},\n created = {2021-11-12T08:30:46.360Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:46.360Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Garciarena, Unai and Santana, Roberto and Mendiburu, Alexander},\n booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference}\n}
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\n \n\n \n \n \n \n \n On the performance of multi-objective estimation of distribution algorithms for combinatorial problems.\n \n \n \n\n\n \n Martins, M., S., R.; El-Yafrani, M.; Santana, R.; Delgado, M.; Lueders, R.; and Ahiod, B.\n\n\n \n\n\n\n In IEEE Congress on Evolutionary Computation (CEC-2018), pages 1-8, 2018. \n \n\n\n\n
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@inproceedings{\n title = {On the performance of multi-objective estimation of distribution algorithms for combinatorial problems},\n type = {inproceedings},\n year = {2018},\n pages = {1-8},\n id = {d1a82970-50e8-34eb-98dc-de54e0b4b605},\n created = {2021-11-12T08:30:53.212Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:53.212Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Martins, M S R and El-Yafrani, M and Santana, R and Delgado, M and Lueders, R and Ahiod, B},\n booktitle = {IEEE Congress on Evolutionary Computation (CEC-2018)}\n}
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\n \n\n \n \n \n \n \n Next generation of Exascale-class systems: ExaNeSt project and the status of its interconnect and storage development.\n \n \n \n\n\n \n Katevenis, M.; Ammendola, R.; Biagioni, A.; Cretaro, P.; Frezza, O.; Cicero, F., L.; Lonardo, A.; Martinelli, M.; Paolucci, P., S.; Pastorelli, E.; Simula, F.; Vicini, P.; Taffoni, G.; Pascual, J., A.; Navaridas, J.; Luján, M.; Goodacre, J.; Lietzow, B.; and Kersten, M., L.\n\n\n \n\n\n\n Microprocessors and Microsystems - Embedded Hardware Design, 61: 58-71. 2018.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{\n title = {Next generation of Exascale-class systems: ExaNeSt project and the status of its interconnect and storage development},\n type = {article},\n year = {2018},\n pages = {58-71},\n volume = {61},\n id = {8829479a-d565-3c30-a7e3-bd4ee5c135a1},\n created = {2021-11-12T08:30:54.522Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:54.522Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Katevenis, Manolis and Ammendola, Roberto and Biagioni, Andrea and Cretaro, Paolo and Frezza, Ottorino and Cicero, Francesca Lo and Lonardo, Alessandro and Martinelli, Michele and Paolucci, Pier Stanislao and Pastorelli, Elena and Simula, Francesco and Vicini, Piero and Taffoni, Giuliano and Pascual, Jose Antonio and Navaridas, Javier and Luján, Mikel and Goodacre, John and Lietzow, Bernd and Kersten, Martin L},\n journal = {Microprocessors and Microsystems - Embedded Hardware Design}\n}
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\n \n\n \n \n \n \n \n Feature extraction-based prediction of tool wear of Inconel 718 in face turning.\n \n \n \n\n\n \n Murua, M.; Suárez, A.; de Lacalle, L., N.; Santana, R.; and Wretland, A.\n\n\n \n\n\n\n Insight-Non-Destructive Testing and Condition Monitoring, 60(8): 443-450. 2018.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{\n title = {Feature extraction-based prediction of tool wear of Inconel 718 in face turning},\n type = {article},\n year = {2018},\n pages = {443-450},\n volume = {60},\n publisher = {The British Institute of Non-Destructive Testing},\n id = {ac823aea-af80-3adc-81e4-4898f78e6a43},\n created = {2021-11-12T08:30:55.054Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:55.054Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Murua, M and Suárez, A and de Lacalle, L N and Santana, R and Wretland, A},\n journal = {Insight-Non-Destructive Testing and Condition Monitoring},\n number = {8}\n}
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\n \n\n \n \n \n \n \n Algorithm 989: Perm_Mateda: A Matlab Toolbox of Estimation of Distribution Algorithms for Permutation-based Combinatorial Optimization Problems.\n \n \n \n\n\n \n Irurozki, E.; Ceberio, J.; Santamaria, J.; Santana, R.; and Mendiburu, A.\n\n\n \n\n\n\n ACM Trans. Math. Softw., 44(4): 47:1–47:13. 5 2018.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Algorithm 989: Perm_Mateda: A Matlab Toolbox of Estimation of Distribution Algorithms for Permutation-based Combinatorial Optimization Problems},\n type = {article},\n year = {2018},\n keywords = {Estimation of distribution algorithms,mallows and generalized mallows models,matlab,optimization,permutation-based problems},\n pages = {47:1–47:13},\n volume = {44},\n month = {5},\n publisher = {ACM},\n id = {f1b84a94-efd8-3d76-b934-c2303fd386d2},\n created = {2021-11-12T08:31:00.256Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:00.256Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Irurozki, Ekhine and Ceberio, Josu and Santamaria, Josean and Santana, Roberto and Mendiburu, Alexander},\n journal = {ACM Trans. Math. Softw.},\n number = {4}\n}
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\n \n\n \n \n \n \n \n Poster: Discover emerging new classes. Balance between supervised and non-supervised classification paradigms.\n \n \n \n\n\n \n Carreno, A.; Iñaki, I.; and Lozano, J., A.\n\n\n \n\n\n\n In BIDAS3: Bilbao Data Science Workshop, 5 2018. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Poster: Discover emerging new classes. Balance between supervised and non-supervised classification paradigms},\n type = {inproceedings},\n year = {2018},\n month = {5},\n id = {ee22beca-7a10-3a03-86fa-9b8f0cb43ed3},\n created = {2021-11-12T08:31:00.526Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:00.526Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Carreno, Ander and Iñaki, Inza and Lozano, Jose A},\n booktitle = {BIDAS3: Bilbao Data Science Workshop}\n}
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\n \n\n \n \n \n \n \n Towards a more efficient representation of imputation operators in TPOT.\n \n \n \n\n\n \n Garciarena, U.; Mendiburu, A.; and Santana, R.\n\n\n \n\n\n\n CoRR, abs/1801.0. 2018.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{\n title = {Towards a more efficient representation of imputation operators in TPOT},\n type = {article},\n year = {2018},\n volume = {abs/1801.0},\n id = {98d880ac-0c00-3143-b5e2-1f8c69c550ec},\n created = {2021-11-12T08:31:01.372Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:01.372Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Garciarena, Unai and Mendiburu, Alexander and Santana, Roberto},\n journal = {CoRR}\n}
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\n \n\n \n \n \n \n \n Hybrid multi-objective Bayesian estimation of distribution algorithm: a comparative analysis for the multi-objective knapsack problem.\n \n \n \n\n\n \n Martins, M., S., R.; Delgado, M., R., B., S.; Lüders, R.; Santana, R.; Gonçalves, R., A.; and de Almeida, C., P.\n\n\n \n\n\n\n Journal of Heuristics, 24(1): 25-47. 2018.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{\n title = {Hybrid multi-objective Bayesian estimation of distribution algorithm: a comparative analysis for the multi-objective knapsack problem},\n type = {article},\n year = {2018},\n pages = {25-47},\n volume = {24},\n id = {2e8212dc-4448-3b5b-ae3a-aae19634212a},\n created = {2021-11-12T08:31:02.166Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:02.166Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Martins, Marcella S R and Delgado, Myriam R B S and Lüders, Ricardo and Santana, Roberto and Gonçalves, Richard Aderbal and de Almeida, Carolina Paula},\n journal = {Journal of Heuristics},\n number = {1}\n}
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\n \n\n \n \n \n \n \n Distance-based Exponential Probability Models on Constrained Combinatorial Optimization Problems.\n \n \n \n\n\n \n Ceberio, J.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n In Proceedings of the Genetic and Evolutionary Computation Conference Companion, of GECCO '18, pages 137-138, 2018. ACM\n \n\n\n\n
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@inproceedings{\n title = {Distance-based Exponential Probability Models on Constrained Combinatorial Optimization Problems},\n type = {inproceedings},\n year = {2018},\n keywords = {constraint,distance-based exponential model,estimation of distribution algorithm,graph partitioning problem},\n pages = {137-138},\n publisher = {ACM},\n series = {GECCO '18},\n id = {cb589744-ee2e-3076-89e1-79a5e1bb7e5c},\n created = {2021-11-12T08:31:03.021Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:03.021Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Ceberio, Josu and Mendiburu, Alexander and Lozano, Jose A},\n booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion}\n}
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\n \n\n \n \n \n \n \n Learning to classify software defects from crowds: A novel approach.\n \n \n \n\n\n \n Hernández-González, J.; Rodríguez, D.; Inza, I.; Harrison, R.; and Lozano, J., A.\n\n\n \n\n\n\n Applied Soft Computing, 62: 579-591. 2018.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{\n title = {Learning to classify software defects from crowds: A novel approach},\n type = {article},\n year = {2018},\n pages = {579-591},\n volume = {62},\n id = {c0fe9b82-3095-30ee-9885-fa4a631713fa},\n created = {2021-11-12T08:31:09.502Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:09.502Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Hernández-González, Jerónimo and Rodríguez, Daniel and Inza, Iñaki and Harrison, Rachel and Lozano, José Antonio},\n journal = {Applied Soft Computing}\n}
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\n \n\n \n \n \n \n \n Early Classification of Time Series by Simultaneously Optimizing the Accuracy and Earliness.\n \n \n \n\n\n \n Mori, U.; Mendiburu, A.; Dasgupta, S.; and Lozano, J., A.\n\n\n \n\n\n\n IEEE Transactions on Neural Networks and Learning Systems, 29(10): 4569-4578. 10 2018.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Early Classification of Time Series by Simultaneously Optimizing the Accuracy and Earliness},\n type = {article},\n year = {2018},\n keywords = {S,cost function,pattern classification,time series},\n pages = {4569-4578},\n volume = {29},\n month = {10},\n id = {7cccc485-dea8-33d0-8db1-d86a8ccf470c},\n created = {2021-11-12T08:31:09.797Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:09.797Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Mori, U and Mendiburu, A and Dasgupta, S and Lozano, J A},\n doi = {10.1109/TNNLS.2017.2764939},\n journal = {IEEE Transactions on Neural Networks and Learning Systems},\n number = {10}\n}
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\n \n\n \n \n \n \n \n Expanding variational autoencoders for learning and exploiting latent representations in search distributions.\n \n \n \n\n\n \n Garciarena, U.; Santana, R.; and Mendiburu, A.\n\n\n \n\n\n\n In Proceedings of the Genetic and Evolutionary Computation Conference, pages 849-856, 2018. \n \n\n\n\n
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@inproceedings{\n title = {Expanding variational autoencoders for learning and exploiting latent representations in search distributions},\n type = {inproceedings},\n year = {2018},\n pages = {849-856},\n id = {aa623abb-41b2-3a4a-b1bc-5d2073d343bc},\n created = {2021-11-12T08:31:17.740Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:17.740Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Garciarena, Unai and Santana, Roberto and Mendiburu, Alexander},\n booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference}\n}
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\n \n\n \n \n \n \n \n Spacecraft trajectory optimization: A review of models, objectives, approaches and solutions.\n \n \n \n\n\n \n Shirazi, A.; Ceberio, J.; and Lozano, J., A.\n\n\n \n\n\n\n Progress in Aerospace Sciences. 2018.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Spacecraft trajectory optimization: A review of models, objectives, approaches and solutions},\n type = {article},\n year = {2018},\n keywords = {Algorithm,Approach,Mathematical model,Metaheuristics,Objective,Optimization,Solution,Trajectory},\n id = {0861edad-54cc-35df-83b9-107b6eb58126},\n created = {2021-11-12T08:31:25.435Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:25.435Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Shirazi, Abolfazl and Ceberio, Josu and Lozano, Jose A},\n journal = {Progress in Aerospace Sciences}\n}
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\n \n\n \n \n \n \n \n A system for airport weather forecasting based on circular regression trees.\n \n \n \n\n\n \n Rozas, P.; Inza, I.; and Lozano, J., A.\n\n\n \n\n\n\n Environmental Modelling and Software, 100: 24-32. 2018.\n \n\n\n\n
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@article{\n title = {A system for airport weather forecasting based on circular regression trees},\n type = {article},\n year = {2018},\n pages = {24-32},\n volume = {100},\n id = {d3ee2399-93c3-38c4-b3c8-dcf785d04df7},\n created = {2021-11-12T08:31:33.032Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:33.032Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Rozas, Pablo and Inza, Iñaki and Lozano, José Antonio},\n journal = {Environmental Modelling and Software}\n}
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\n \n\n \n \n \n \n \n An investigation of the selection strategies impact on MOEDAs: CMA-ES and UMDA.\n \n \n \n\n\n \n Strickler, A.; Junior, O., R., C.; Pozo, A., T., R.; and Santana, R.\n\n\n \n\n\n\n Applied Soft Computing, 62: 963-973. 2018.\n \n\n\n\n
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@article{\n title = {An investigation of the selection strategies impact on MOEDAs: CMA-ES and UMDA},\n type = {article},\n year = {2018},\n pages = {963-973},\n volume = {62},\n id = {5120dbc8-7dea-30a7-b427-84cb57f6da8d},\n created = {2021-11-12T08:31:33.639Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:33.639Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Strickler, Andrei and Junior, Olacir Rodrigues Castro and Pozo, Aurora T R and Santana, Roberto},\n journal = {Applied Soft Computing}\n}
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\n \n\n \n \n \n \n \n Crowd Learning with Candidate Labeling: An EM-Based Solution.\n \n \n \n\n\n \n Benaran-Munoz, I.; Hernández-González, J.; and Pérez, A.\n\n\n \n\n\n\n In Conference of the Spanish Association for Artificial Intelligence, pages 13-23, 2018. \n \n\n\n\n
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@inproceedings{\n title = {Crowd Learning with Candidate Labeling: An EM-Based Solution},\n type = {inproceedings},\n year = {2018},\n pages = {13-23},\n id = {3c7c9669-5d45-3fc2-a673-2c20d815d821},\n created = {2021-11-12T08:31:36.194Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:36.194Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Benaran-Munoz, Iker and Hernández-González, Jerónimo and Pérez, Aritz},\n booktitle = {Conference of the Spanish Association for Artificial Intelligence}\n}
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\n \n\n \n \n \n \n \n The Relationship Between Graphical Representations of Regular Vine Copulas and Polytrees.\n \n \n \n\n\n \n Carrera, D.; Santana, R.; and Lozano, J., A.\n\n\n \n\n\n\n In International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, pages 678-690, 2018. \n \n\n\n\n
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@inproceedings{\n title = {The Relationship Between Graphical Representations of Regular Vine Copulas and Polytrees},\n type = {inproceedings},\n year = {2018},\n pages = {678-690},\n id = {65cfb355-a334-383a-b04a-e02780f89632},\n created = {2021-11-12T08:31:42.331Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:42.331Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Carrera, Diana and Santana, Roberto and Lozano, Jose A},\n booktitle = {International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems}\n}
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\n \n\n \n \n \n \n \n Analysis of the Complexity of the Automatic Pipeline Generation Problem.\n \n \n \n\n\n \n Garciarena, U.; Santana, R.; and Mendiburu, A.\n\n\n \n\n\n\n In Proceedings of the 2018 IEEE Congress on Evolutionary Computation (CEC), pages 1841-1849, 2018. \n \n\n\n\n
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@inproceedings{\n title = {Analysis of the Complexity of the Automatic Pipeline Generation Problem},\n type = {inproceedings},\n year = {2018},\n pages = {1841-1849},\n id = {2c1166d7-0a3a-3e4e-9cce-309d168bacde},\n created = {2021-11-12T08:31:43.825Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:43.825Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Garciarena, Unai and Santana, Roberto and Mendiburu, Alexander},\n booktitle = {Proceedings of the 2018 IEEE Congress on Evolutionary Computation (CEC)}\n}
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\n \n\n \n \n \n \n \n Bayesian Inference for Algorithm Ranking Analysis.\n \n \n \n\n\n \n Calvo, B.; Ceberio, J.; and Lozano, J., A.\n\n\n \n\n\n\n In Proceedings of the Genetic and Evolutionary Computation Conference Companion, of GECCO '18, pages 324-325, 2018. ACM\n \n\n\n\n
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@inproceedings{\n title = {Bayesian Inference for Algorithm Ranking Analysis},\n type = {inproceedings},\n year = {2018},\n keywords = {algorithm comparison,bayesian analysis,plackett-luce model,ranking models,statistical analysis},\n pages = {324-325},\n publisher = {ACM},\n series = {GECCO '18},\n id = {38809bac-0b41-3936-9f4c-d955bfbf9b90},\n created = {2021-11-12T08:31:50.693Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:50.693Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Calvo, Borja and Ceberio, Josu and Lozano, Jose A},\n booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion}\n}
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\n \n\n \n \n \n \n \n A CAM-Free Exascalable HPC Router for Low-Energy Communications.\n \n \n \n\n\n \n Concatto, C.; Pascual, J., A.; Navaridas, J.; Lant, J.; Attwood, A.; Luján, M.; and Goodacre, J.\n\n\n \n\n\n\n In Berekovic, M.; Buchty, R.; Hamann, H.; Koch, D.; and Pionteck, T., editor(s), Architecture of Computing Systems - ARCS 2018 - 31st International Conference, Braunschweig, Germany, April 9-12, 2018, Proceedings, volume 10793, of Lecture Notes in Computer Science, pages 99-111, 2018. Springer\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{\n title = {A CAM-Free Exascalable HPC Router for Low-Energy Communications},\n type = {inproceedings},\n year = {2018},\n pages = {99-111},\n volume = {10793},\n publisher = {Springer},\n series = {Lecture Notes in Computer Science},\n id = {09066085-32d5-393f-b8e5-0152335d0faf},\n created = {2021-11-12T08:31:51.526Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:51.526Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Concatto, Caroline and Pascual, Jose Antonio and Navaridas, Javier and Lant, Joshua and Attwood, Andrew and Luján, Mikel and Goodacre, John},\n editor = {Berekovic, Mladen and Buchty, Rainer and Hamann, Heiko and Koch, Dirk and Pionteck, Thilo},\n booktitle = {Architecture of Computing Systems - ARCS 2018 - 31st International Conference, Braunschweig, Germany, April 9-12, 2018, Proceedings}\n}
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\n \n\n \n \n \n \n \n Exploring the probabilistic graphic model of a hybrid multi-objective Bayesian estimation of distribution algorithm.\n \n \n \n\n\n \n Martins, M., S., R.; Delgado, M., R., B., S.; Lüders, R.; Santana, R.; Gonçalves, R., A.; and de Almeida, C., P.\n\n\n \n\n\n\n Applied Soft Computing, 73: 328-343. 2018.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Exploring the probabilistic graphic model of a hybrid multi-objective Bayesian estimation of distribution algorithm},\n type = {article},\n year = {2018},\n pages = {328-343},\n volume = {73},\n id = {fc4ca0ea-483c-30ba-a3b3-8419a0994eb3},\n created = {2021-11-12T08:31:57.581Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:57.581Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Martins, Marcella S R and Delgado, Myriam R B S and Lüders, Ricardo and Santana, Roberto and Gonçalves, Richard Aderbal and de Almeida, Carolina Paula},\n journal = {Applied Soft Computing}\n}
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\n \n\n \n \n \n \n \n A decomposition-based kernel of Mallows models algorithm for bi- and tri-objective permutation flowshop scheduling problem.\n \n \n \n\n\n \n Zangari, M.; Constantino, A., A.; and Ceberio, J.\n\n\n \n\n\n\n Applied Soft Computing, 71: 526-537. 2018.\n \n\n\n\n
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@article{\n title = {A decomposition-based kernel of Mallows models algorithm for bi- and tri-objective permutation flowshop scheduling problem},\n type = {article},\n year = {2018},\n keywords = {Decomposition approach,Flowshop scheduling,Mallows model,Multi-objective optimization},\n pages = {526-537},\n volume = {71},\n id = {f88de466-ee5a-34ea-a9f5-893f1d1786a8},\n created = {2021-11-12T08:31:59.985Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:59.985Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Zangari, Murilo and Constantino, Ademir Aparecido and Ceberio, Josu},\n journal = {Applied Soft Computing}\n}
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\n \n\n \n \n \n \n \n \n Multi-start Methods BT Handbook of Heuristics.\n \n \n \n \n\n\n \n Martí, R.; Lozano, J., A.; Mendiburu, A.; and Hernando, L.\n\n\n \n\n\n\n pages 155-175. Martí, R.; Pardalos, P., M.; and Resende, M., G., C., editor(s). Springer International Publishing, 2018.\n \n\n\n\n
\n\n\n\n \n \n \"Website\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inbook{\n type = {inbook},\n year = {2018},\n pages = {155-175},\n websites = {https://doi.org/10.1007/978-3-319-07124-4_1},\n publisher = {Springer International Publishing},\n city = {Cham},\n id = {beea1feb-7090-3a43-bb6d-fc9d52ed3c4c},\n created = {2021-11-12T08:32:02.895Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:02.895Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {incollection},\n private_publication = {false},\n abstract = {Multi-start procedures were originally conceived as a way to exploit a local or neighborhood search procedure, by simply applying it from multiple random initial solutions. Modern multi-start methods usually incorporate a powerful form of diversification in the generation of solutions to help overcome local optimality. Different metaheuristics, such as GRASP or tabu search, have been applied to this end. This survey briefly sketches historical developments that have motivated the field and then focuses on modern contributions that define the current state of the art. Two classical categories of multi-start methods are considered according to their domain of application: global optimization and combinatorial optimization. Additionally, several methods are reviewed to estimate the number of local optima in combinatorial problems. The estimation of this number can help to establish the complexity of a given instance, and also to choose the most convenient neighborhood, which is especially interesting in the context of multi-start methods. Experiments on three well-known combinatorial optimization problems are included to illustrate the local optima estimation techniques.},\n bibtype = {inbook},\n author = {Martí, Rafael and Lozano, Jose A and Mendiburu, Alexander and Hernando, Leticia},\n editor = {Martí, Rafael and Pardalos, Panos M and Resende, Mauricio G C},\n doi = {10.1007/978-3-319-07124-4_1},\n chapter = {Multi-start Methods BT Handbook of Heuristics}\n}
\n
\n\n\n
\n Multi-start procedures were originally conceived as a way to exploit a local or neighborhood search procedure, by simply applying it from multiple random initial solutions. Modern multi-start methods usually incorporate a powerful form of diversification in the generation of solutions to help overcome local optimality. Different metaheuristics, such as GRASP or tabu search, have been applied to this end. This survey briefly sketches historical developments that have motivated the field and then focuses on modern contributions that define the current state of the art. Two classical categories of multi-start methods are considered according to their domain of application: global optimization and combinatorial optimization. Additionally, several methods are reviewed to estimate the number of local optima in combinatorial problems. The estimation of this number can help to establish the complexity of a given instance, and also to choose the most convenient neighborhood, which is especially interesting in the context of multi-start methods. Experiments on three well-known combinatorial optimization problems are included to illustrate the local optima estimation techniques.\n
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\n \n\n \n \n \n \n \n \n Evolutionary Multi-Objective System Design: Theory and Applications. Computer and Information Science Series.\n \n \n \n \n\n\n \n Lima, R., H., R.; Fontoura, V.; Pozo, A., T., R.; and Santana, R.\n\n\n \n\n\n\n pages 151-170. Nedjah, N.; De-Macedo-Mourelle, L.; and Silverio-Lopes, H., editor(s). Chapman /& Hall/CRC, 2018.\n \n\n\n\n
\n\n\n\n \n \n \"Website\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inbook{\n type = {inbook},\n year = {2018},\n pages = {151-170},\n websites = {https://www.taylorfrancis.com/chapters/edit/10.1201/9781315366845-8/multi-objective-approach-protein-structure-prediction-problem-ricardo-lima-vidal-fontoura-aurora-pozo-roberto-santana},\n publisher = {Chapman /& Hall/CRC},\n chapter = {Evolutionary Multi-Objective System Design: Theory and Applications. Computer and Information Science Series},\n id = {39af6811-4c23-31b2-89d6-2a4c20b0684f},\n created = {2021-11-12T08:32:03.172Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:03.172Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {incollection},\n private_publication = {false},\n bibtype = {inbook},\n author = {Lima, R H R and Fontoura, V and Pozo, A T R and Santana, R},\n editor = {Nedjah, N and De-Macedo-Mourelle, L and Silverio-Lopes, H}\n}
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\n \n\n \n \n \n \n \n Fitting the data from embryo implantation prediction: Learning from label proportions.\n \n \n \n\n\n \n Hernández-González, J.; Inza, I.; Crisol-Ort\\'\\iz, L.; Guembe, M., A.; Iñarra, M., J.; and Lozano, J., A.\n\n\n \n\n\n\n Statistical methods in medical research, 27(4): 1056-1066. 2018.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{\n title = {Fitting the data from embryo implantation prediction: Learning from label proportions},\n type = {article},\n year = {2018},\n pages = {1056-1066},\n volume = {27},\n publisher = {SAGE Publications Sage UK: London, England},\n id = {0441206a-0674-35fe-a552-a3c15b70e48e},\n created = {2021-11-12T08:32:10.447Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:10.447Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Hernández-González, Jerónimo and Inza, Inaki and Crisol-Ort\\'\\iz, Lorena and Guembe, Mar\\'\\ia A and Iñarra, Mar\\'\\ia J and Lozano, Jose A},\n journal = {Statistical methods in medical research},\n number = {4}\n}
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\n \n\n \n \n \n \n \n Two datasets of defect reports labeled by a crowd of annotators of unknown reliability.\n \n \n \n\n\n \n Hernández-González, J.; Rodriguez, D.; Inza, I.; Harrison, R.; and Lozano, J., A.\n\n\n \n\n\n\n Data in brief, 18: 840-845. 2018.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{\n title = {Two datasets of defect reports labeled by a crowd of annotators of unknown reliability},\n type = {article},\n year = {2018},\n pages = {840-845},\n volume = {18},\n publisher = {Elsevier},\n id = {8d2c8529-3591-3cca-8f2f-5b960310d016},\n created = {2021-11-12T08:32:15.929Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:15.929Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Hernández-González, Jerónimo and Rodriguez, Daniel and Inza, Iñaki and Harrison, Rachel and Lozano, Jose A},\n journal = {Data in brief}\n}
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\n \n\n \n \n \n \n \n A decomposition-based binary ACO algorithm for the multiobjective UBQP.\n \n \n \n\n\n \n Zangari, M.; Pozo, A., T., R.; Santana, R.; and Mendiburu, A.\n\n\n \n\n\n\n Neurocomputing, 246: 58-68. 2017.\n \n\n\n\n
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@article{\n title = {A decomposition-based binary ACO algorithm for the multiobjective UBQP},\n type = {article},\n year = {2017},\n pages = {58-68},\n volume = {246},\n id = {3a535736-fc3c-3495-b398-b166bdb3050a},\n created = {2021-11-12T08:30:08.901Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:08.901Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Zangari, Murilo and Pozo, Aurora T R and Santana, Roberto and Mendiburu, Alexander},\n journal = {Neurocomputing}\n}
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\n \n\n \n \n \n \n \n On-Line Dynamic Time Warping for Streaming Time Series.\n \n \n \n\n\n \n Oregi, I.; Pérez, A.; Ser, J., D.; and Lozano, J., A.\n\n\n \n\n\n\n In Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18-22, 2017, Proceedings, Part II, pages 591-605, 2017. \n \n\n\n\n
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@inproceedings{\n title = {On-Line Dynamic Time Warping for Streaming Time Series},\n type = {inproceedings},\n year = {2017},\n pages = {591-605},\n id = {222fa9dc-4a1c-3cf2-8f5d-887996598b8c},\n created = {2021-11-12T08:30:12.628Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:12.628Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Oregi, Izaskun and Pérez, Aritz and Ser, Javier Del and Lozano, José Antonio},\n booktitle = {Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18-22, 2017, Proceedings, Part II}\n}
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\n \n\n \n \n \n \n \n A comparison of probabilistic-based optimization approaches for vehicle routing problems.\n \n \n \n\n\n \n Santana, R.; Sirbiladze, G.; Ghvaberidze, B.; and Matsaberidze, B.\n\n\n \n\n\n\n In 2017 IEEE Congress on Evolutionary Computation, CEC 2017, pages 2606-2613, 2017. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{\n title = {A comparison of probabilistic-based optimization approaches for vehicle routing problems},\n type = {inproceedings},\n year = {2017},\n pages = {2606-2613},\n id = {bade32b1-693a-3a52-b7d8-e607de8a2a92},\n created = {2021-11-12T08:30:17.597Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:17.597Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Santana, Roberto and Sirbiladze, Gia and Ghvaberidze, Bezhan and Matsaberidze, Bidzina},\n booktitle = {2017 IEEE Congress on Evolutionary Computation, CEC 2017}\n}
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\n \n\n \n \n \n \n \n Evolutionary algorithms to optimize low-thrust trajectory design in spacecraft orbital precession mission.\n \n \n \n\n\n \n Shirazi, A.; Ceberio, J.; and Lozano, J., A.\n\n\n \n\n\n\n In 2017 IEEE Congress on Evolutionary Computation, CEC 2017, Donostia, San Sebastián, Spain, June 5-8, 2017, pages 1779-1786, 2017. \n \n\n\n\n
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@inproceedings{\n title = {Evolutionary algorithms to optimize low-thrust trajectory design in spacecraft orbital precession mission},\n type = {inproceedings},\n year = {2017},\n pages = {1779-1786},\n id = {941095e4-56e0-3bae-bf12-5f661db5e889},\n created = {2021-11-12T08:30:18.904Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:18.904Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Shirazi, Abolfazl and Ceberio, Josu and Lozano, José Antonio},\n booktitle = {2017 IEEE Congress on Evolutionary Computation, CEC 2017, Donostia, San Sebastián, Spain, June 5-8, 2017}\n}
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\n \n\n \n \n \n \n \n Different scenarios for survival analysis of evolutionary algorithms.\n \n \n \n\n\n \n Santana, R.; and Lozano, J., A.\n\n\n \n\n\n\n In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2017, pages 825-832, 2017. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{\n title = {Different scenarios for survival analysis of evolutionary algorithms},\n type = {inproceedings},\n year = {2017},\n pages = {825-832},\n id = {8a69d3bd-bc0c-31fb-9ac1-911808a93706},\n created = {2021-11-12T08:30:19.823Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:19.823Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Santana, Roberto and Lozano, José Antonio},\n booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2017}\n}
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\n \n\n \n \n \n \n \n The Weighted Independent Domination Problem: ILP Model and Algorithmic Approaches.\n \n \n \n\n\n \n Davidson, P., P.; Blum, C.; and Lozano, J., A.\n\n\n \n\n\n\n In Evolutionary Computation in Combinatorial Optimization - 17th European Conference, EvoCOP 2017, Amsterdam, The Netherlands, April 19-21, 2017, Proceedings, pages 201-214, 2017. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{\n title = {The Weighted Independent Domination Problem: ILP Model and Algorithmic Approaches},\n type = {inproceedings},\n year = {2017},\n pages = {201-214},\n id = {a57f8d97-65a4-3cbd-ba4c-adb598a8803c},\n created = {2021-11-12T08:30:20.935Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:20.935Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Davidson, Pedro Pinacho and Blum, Christian and Lozano, José Antonio},\n booktitle = {Evolutionary Computation in Combinatorial Optimization - 17th European Conference, EvoCOP 2017, Amsterdam, The Netherlands, April 19-21, 2017, Proceedings}\n}
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\n \n\n \n \n \n \n \n \n Reproducing and learning new algebraic operations on word embeddings using genetic programming.\n \n \n \n \n\n\n \n Santana, R.\n\n\n \n\n\n\n CoRR, abs/1702.0. 2017.\n \n\n\n\n
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@article{\n title = {Reproducing and learning new algebraic operations on word embeddings using genetic programming},\n type = {article},\n year = {2017},\n volume = {abs/1702.0},\n websites = {http://arxiv.org/abs/1702.05624},\n id = {c9afce73-5590-3d50-ad89-837201c29d1a},\n created = {2021-11-12T08:30:24.830Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:24.830Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Santana, Roberto},\n journal = {CoRR}\n}
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\n \n\n \n \n \n \n \n Multiobjective decomposition-based Mallows Models estimation of distribution algorithm. A case of study for permutation flowshop scheduling problem.\n \n \n \n\n\n \n Zangari, M.; Mendiburu, A.; Santana, R.; and Pozo, A., T., R.\n\n\n \n\n\n\n Information Sciences, 397: 137-154. 2017.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{\n title = {Multiobjective decomposition-based Mallows Models estimation of distribution algorithm. A case of study for permutation flowshop scheduling problem},\n type = {article},\n year = {2017},\n pages = {137-154},\n volume = {397},\n id = {99dcf448-84ba-3f87-96b2-d392a79da122},\n created = {2021-11-12T08:30:27.298Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:27.298Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Zangari, Murilo and Mendiburu, Alexander and Santana, Roberto and Pozo, Aurora T R},\n journal = {Information Sciences}\n}
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\n \n\n \n \n \n \n \n On the Effects of Data-Aware Allocation on Fully Distributed Storage Systems for Exascale.\n \n \n \n\n\n \n Pascual, J., A.; Concatto, C.; Lant, J.; and Navaridas, J.\n\n\n \n\n\n\n In Heras, D., B.; Bougé, L.; Mencagli, G.; Jeannot, E.; Sakellariou, R.; Badia, R., M.; Barbosa, J., G.; Ricci, L.; Scott, S., L.; Lankes, S.; and Weidendorfer, J., editor(s), Euro-Par 2017: Parallel Processing Workshops - Euro-Par 2017 International Workshops, Santiago de Compostela, Spain, August 28-29, 2017, Revised Selected Papers, volume 10659, of Lecture Notes in Computer Science, pages 725-736, 2017. Springer\n \n\n\n\n
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@inproceedings{\n title = {On the Effects of Data-Aware Allocation on Fully Distributed Storage Systems for Exascale},\n type = {inproceedings},\n year = {2017},\n pages = {725-736},\n volume = {10659},\n publisher = {Springer},\n series = {Lecture Notes in Computer Science},\n id = {70b516e0-8560-385a-b916-e9db937089b6},\n created = {2021-11-12T08:30:30.877Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:30.877Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Pascual, Jose Antonio and Concatto, Caroline and Lant, Joshua and Navaridas, Javier},\n editor = {Heras, Dora Blanco and Bougé, Luc and Mencagli, Gabriele and Jeannot, Emmanuel and Sakellariou, Rizos and Badia, Rosa M and Barbosa, Jorge G and Ricci, Laura and Scott, Stephen L and Lankes, Stefan and Weidendorfer, Josef},\n booktitle = {Euro-Par 2017: Parallel Processing Workshops - Euro-Par 2017 International Workshops, Santiago de Compostela, Spain, August 28-29, 2017, Revised Selected Papers}\n}
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\n \n\n \n \n \n \n \n Automated design of hyper-heuristics components to solve the PSP problem with HP model.\n \n \n \n\n\n \n Fontoura, V., D.; Pozo, A., T., R.; and Santana, R.\n\n\n \n\n\n\n In 2017 IEEE Congress on Evolutionary Computation, CEC 2017, pages 1848-1855, 2017. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{\n title = {Automated design of hyper-heuristics components to solve the PSP problem with HP model},\n type = {inproceedings},\n year = {2017},\n pages = {1848-1855},\n id = {f94c56d8-8e45-3f32-93c0-29e4df4fd275},\n created = {2021-11-12T08:30:36.171Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:36.171Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Fontoura, Vidal D and Pozo, Aurora T R and Santana, Roberto},\n booktitle = {2017 IEEE Congress on Evolutionary Computation, CEC 2017}\n}
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\n \n\n \n \n \n \n \n Measuring the class-imbalance extent of multi-class problems.\n \n \n \n\n\n \n Ortigosa-Hernández, J.; Inza, I.; and Lozano, J., A.\n\n\n \n\n\n\n Pattern Recognition Letters, 98: 32-38. 2017.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{\n title = {Measuring the class-imbalance extent of multi-class problems},\n type = {article},\n year = {2017},\n pages = {32-38},\n volume = {98},\n id = {b2b47d45-f933-355d-9226-8716d290a63b},\n created = {2021-11-12T08:30:36.987Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:36.987Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Ortigosa-Hernández, Jonathan and Inza, Iñaki and Lozano, José Antonio},\n journal = {Pattern Recognition Letters}\n}
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\n \n\n \n \n \n \n \n \n Gray-box optimization and factorized distribution algorithms: where two worlds collide.\n \n \n \n \n\n\n \n Santana, R.\n\n\n \n\n\n\n CoRR, abs/1707.0. 2017.\n \n\n\n\n
\n\n\n\n \n \n \"Gray-boxWebsite\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{\n title = {Gray-box optimization and factorized distribution algorithms: where two worlds collide},\n type = {article},\n year = {2017},\n volume = {abs/1707.0},\n websites = {http://arxiv.org/abs/1707.03093},\n id = {d28e20d6-21e8-377b-b3b4-5c73ad9fd255},\n created = {2021-11-12T08:30:41.187Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:41.187Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Santana, Roberto},\n journal = {CoRR}\n}
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\n \n\n \n \n \n \n \n \n Reliable early classification of time series based on discriminating the classes over time.\n \n \n \n \n\n\n \n Mori, U.; Mendiburu, A.; Keogh, E., J.; and Lozano, J., A.\n\n\n \n\n\n\n Data Min. Knowl. Discov., 31(1): 233-263. 2017.\n \n\n\n\n
\n\n\n\n \n \n \"ReliableWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{\n title = {Reliable early classification of time series based on discriminating the classes over time},\n type = {article},\n year = {2017},\n pages = {233-263},\n volume = {31},\n websites = {https://doi.org/10.1007/s10618-016-0462-1},\n id = {ec6b9b6c-2957-3603-94ce-e7df708a920f},\n created = {2021-11-12T08:30:42.870Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:42.870Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Mori, Usue and Mendiburu, Alexander and Keogh, Eamonn J and Lozano, José Antonio},\n doi = {10.1007/s10618-016-0462-1},\n journal = {Data Min. Knowl. Discov.},\n number = {1}\n}
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\n \n\n \n \n \n \n \n Combining CMA-ES and MOEA/DD for many-objective optimization.\n \n \n \n\n\n \n Castro, O., R.; Santana, R.; Lozano, J., A.; and Pozo, A., T., R.\n\n\n \n\n\n\n In 2017 IEEE Congress on Evolutionary Computation, CEC 2017, pages 1451-1458, 2017. \n \n\n\n\n
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@inproceedings{\n title = {Combining CMA-ES and MOEA/DD for many-objective optimization},\n type = {inproceedings},\n year = {2017},\n pages = {1451-1458},\n id = {e3a28c5d-f83f-32f8-831e-d32f89ce5d2d},\n created = {2021-11-12T08:30:53.729Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:53.729Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Castro, Olacir R and Santana, Roberto and Lozano, José Antonio and Pozo, Aurora T R},\n booktitle = {2017 IEEE Congress on Evolutionary Computation, CEC 2017}\n}
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\n \n\n \n \n \n \n \n The Next Generation of Exascale-Class Systems: The ExaNeSt Project.\n \n \n \n\n\n \n Ammendola, R.; Biagioni, A.; Cretaro, P.; Frezza, O.; Cicero, F., L.; Lonardo, A.; Martinelli, M.; Paolucci, P., S.; Pastorelli, E.; Simula, F.; Vicini, P.; Taffoni, G.; Pascual, J., A.; Navaridas, J.; Luján, M.; Goodacre, J.; Chrysos, N.; and Katevenis, M.\n\n\n \n\n\n\n In Kubátová, H.; Novotný, M.; and Skavhaug, A., editor(s), Euromicro Conference on Digital System Design, DSD 2017, Vienna, Austria, August 30 - Sept. 1, 2017, pages 510-515, 2017. IEEE Computer Society\n \n\n\n\n
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@inproceedings{\n title = {The Next Generation of Exascale-Class Systems: The ExaNeSt Project},\n type = {inproceedings},\n year = {2017},\n pages = {510-515},\n publisher = {IEEE Computer Society},\n id = {63168816-dc2e-31b7-aefb-e22825eea9c1},\n created = {2021-11-12T08:30:58.529Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:58.529Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Ammendola, Roberto and Biagioni, Andrea and Cretaro, Paolo and Frezza, Ottorino and Cicero, Francesca Lo and Lonardo, Alessandro and Martinelli, Michele and Paolucci, Pier Stanislao and Pastorelli, Elena and Simula, Francesco and Vicini, Piero and Taffoni, Giuliano and Pascual, Jose Antonio and Navaridas, Javier and Luján, Mikel and Goodacre, John and Chrysos, Nikolaos and Katevenis, Manolis},\n editor = {Kubátová, Hana and Novotný, Martin and Skavhaug, Amund},\n booktitle = {Euromicro Conference on Digital System Design, DSD 2017, Vienna, Austria, August 30 - Sept. 1, 2017}\n}
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\n \n\n \n \n \n \n \n Local Optima Networks of the Permutation Flowshop Scheduling Probelm: Makespan vs. Total Flow Time.\n \n \n \n\n\n \n Hernando, L.; Daolio, F.; Veerapen, N.; and Ochoa, G.\n\n\n \n\n\n\n In Proceeding of 2017 IEEE Congress on Evolutionary Computation, pages 1964-1971, 2017. \n \n\n\n\n
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@inproceedings{\n title = {Local Optima Networks of the Permutation Flowshop Scheduling Probelm: Makespan vs. Total Flow Time},\n type = {inproceedings},\n year = {2017},\n pages = {1964-1971},\n id = {bf2e2fd1-26dc-3ef0-a81c-6ce1fc9b3bba},\n created = {2021-11-12T08:31:05.168Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:05.168Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Hernando, Leticia and Daolio, Fabio and Veerapen, Nadarajen and Ochoa, Gabriela},\n booktitle = {Proceeding of 2017 IEEE Congress on Evolutionary Computation}\n}
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\n \n\n \n \n \n \n \n Designing an exascale interconnect using multi-objective optimization.\n \n \n \n\n\n \n Pascual, J., A.; Lant, J.; Attwood, A.; Concatto, C.; Navaridas, J.; Luján, M.; and Goodacre, J.\n\n\n \n\n\n\n In 2017 IEEE Congress on Evolutionary Computation, CEC 2017, Donostia, San Sebastián, Spain, June 5-8, 2017, pages 2209-2216, 2017. IEEE\n \n\n\n\n
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@inproceedings{\n title = {Designing an exascale interconnect using multi-objective optimization},\n type = {inproceedings},\n year = {2017},\n pages = {2209-2216},\n publisher = {IEEE},\n id = {faf9ab51-969e-33cd-99ac-8d0bd448e57b},\n created = {2021-11-12T08:31:05.701Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:05.701Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Pascual, Jose Antonio and Lant, Joshua and Attwood, Andrew and Concatto, Caroline and Navaridas, Javier and Luján, Mikel and Goodacre, John},\n booktitle = {2017 IEEE Congress on Evolutionary Computation, CEC 2017, Donostia, San Sebastián, Spain, June 5-8, 2017}\n}
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\n \n\n \n \n \n \n \n Learning from Proportions of Positive and Unlabeled Examples.\n \n \n \n\n\n \n Hernández-González, J.; Inza, I.; and Lozano, J., A.\n\n\n \n\n\n\n Int. J. Intell. Syst., 32(2): 109-133. 2017.\n \n\n\n\n
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@article{\n title = {Learning from Proportions of Positive and Unlabeled Examples},\n type = {article},\n year = {2017},\n pages = {109-133},\n volume = {32},\n id = {30485ebb-90f2-379d-9187-62c0613c9ed6},\n created = {2021-11-12T08:31:07.316Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:07.316Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Hernández-González, Jerónimo and Inza, Iñaki and Lozano, José Antonio},\n journal = {Int. J. Intell. Syst.},\n number = {2}\n}
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\n \n\n \n \n \n \n \n Transfer weight functions for injecting problem information in the multi-objective CMA-ES.\n \n \n \n\n\n \n Jr., O., R., C.; Pozo, A., T., R.; Lozano, J., A.; and Santana, R.\n\n\n \n\n\n\n Memetic Computing, 9(2): 153-180. 2017.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Transfer weight functions for injecting problem information in the multi-objective CMA-ES},\n type = {article},\n year = {2017},\n pages = {153-180},\n volume = {9},\n id = {53d883eb-8e09-3088-9697-c4580175a242},\n created = {2021-11-12T08:31:08.181Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:08.181Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Jr., Olacir R Castro and Pozo, Aurora T R and Lozano, José Antonio and Santana, Roberto},\n journal = {Memetic Computing},\n number = {2}\n}
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\n \n\n \n \n \n \n \n An efficient approximation to the K-means clustering for massive data.\n \n \n \n\n\n \n Capó, M.; Pérez, A.; and Lozano, J., A.\n\n\n \n\n\n\n Knowl.-Based Syst., 117: 56-69. 2017.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {An efficient approximation to the K-means clustering for massive data},\n type = {article},\n year = {2017},\n pages = {56-69},\n volume = {117},\n id = {8dfa55f3-7b6d-3d4f-a35b-ad540395b383},\n created = {2021-11-12T08:31:16.319Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:16.319Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Capó, Marco and Pérez, Aritz and Lozano, José Antonio},\n journal = {Knowl.-Based Syst.}\n}
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\n \n\n \n \n \n \n \n An investigation of clustering strategies in many-objective optimization: the I-Multi algorithm as a case study.\n \n \n \n\n\n \n Jr., O., R., C.; Pozo, A., T., R.; Lozano, J., A.; and Santana, R.\n\n\n \n\n\n\n Swarm Intelligence, 11(2): 101-130. 2017.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {An investigation of clustering strategies in many-objective optimization: the I-Multi algorithm as a case study},\n type = {article},\n year = {2017},\n pages = {101-130},\n volume = {11},\n id = {934a7802-4347-3dea-9d02-11e1f4c986ef},\n created = {2021-11-12T08:31:16.587Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:16.587Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Jr., Olacir R Castro and Pozo, Aurora T R and Lozano, José Antonio and Santana, Roberto},\n journal = {Swarm Intelligence},\n number = {2}\n}
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\n \n\n \n \n \n \n \n A square lattice probability model for optimising the Graph Partitioning Problem.\n \n \n \n\n\n \n Ceberio, J.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n In 2017 IEEE Congress on Evolutionary Computation, CEC 2017, Donostia, San Sebastián, Spain, June 5-8, 2017, pages 1629-1636, 2017. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{\n title = {A square lattice probability model for optimising the Graph Partitioning Problem},\n type = {inproceedings},\n year = {2017},\n pages = {1629-1636},\n id = {aa049464-bd7c-3fda-8751-e29ad4291aaf},\n created = {2021-11-12T08:31:16.898Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:16.898Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Ceberio, Josu and Mendiburu, Alexander and Lozano, José Antonio},\n booktitle = {2017 IEEE Congress on Evolutionary Computation, CEC 2017, Donostia, San Sebastián, Spain, June 5-8, 2017}\n}
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\n \n\n \n \n \n \n \n \n Evolving imputation strategies for missing data in classification problems with TPOT.\n \n \n \n \n\n\n \n Garciarena, U.; Santana, R.; and Mendiburu, A.\n\n\n \n\n\n\n CoRR, abs/1706.0. 2017.\n \n\n\n\n
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@article{\n title = {Evolving imputation strategies for missing data in classification problems with TPOT},\n type = {article},\n year = {2017},\n volume = {abs/1706.0},\n websites = {http://arxiv.org/abs/1706.01120},\n id = {91d52316-8d05-31ba-a00b-4ce82163e3be},\n created = {2021-11-12T08:31:23.250Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:23.250Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Garciarena, Unai and Santana, Roberto and Mendiburu, Alexander},\n journal = {CoRR}\n}
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\n \n\n \n \n \n \n \n Improved routing algorithms in the dual-port datacenter networks HCN and BCN.\n \n \n \n\n\n \n Erickson, A.; Stewart, I., A.; Pascual, J., A.; and Navaridas, J.\n\n\n \n\n\n\n Future Generation Comp. Syst., 75: 58-71. 2017.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{\n title = {Improved routing algorithms in the dual-port datacenter networks HCN and BCN},\n type = {article},\n year = {2017},\n pages = {58-71},\n volume = {75},\n id = {8eac5d96-878c-3575-80c1-4389e7d2cead},\n created = {2021-11-12T08:31:32.764Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:32.764Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Erickson, Alejandro and Stewart, Iain A and Pascual, Jose Antonio and Navaridas, Javier},\n journal = {Future Generation Comp. Syst.}\n}
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\n \n\n \n \n \n \n \n \n General chair's welcome.\n \n \n \n \n\n\n \n Lozano, J., A.\n\n\n \n\n\n\n In 2017 IEEE Congress on Evolutionary Computation, CEC 2017, Donostia, San Sebastián, Spain, June 5-8, 2017, 2017. \n \n\n\n\n
\n\n\n\n \n \n \"GeneralWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{\n title = {General chair's welcome},\n type = {inproceedings},\n year = {2017},\n websites = {https://doi.org/10.1109/CEC.2017.7969283},\n id = {2a1b91f7-133c-3e84-b2f2-2c2226f10fcd},\n created = {2021-11-12T08:31:33.922Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:33.922Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Lozano, José Antonio},\n doi = {10.1109/CEC.2017.7969283},\n booktitle = {2017 IEEE Congress on Evolutionary Computation, CEC 2017, Donostia, San Sebastián, Spain, June 5-8, 2017}\n}
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\n \n\n \n \n \n \n \n Probabilistic Analysis of Pareto Front Approximation for a Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm.\n \n \n \n\n\n \n Martins, M., S., R.; Delgado, M., R.; Lüders, R.; Santana, R.; Gonçalves, R., A.; and de Almeida, C., P.\n\n\n \n\n\n\n In 2017 Brazilian Conference on Intelligent Systems, BRACIS 2017, pages 384-389, 2017. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Probabilistic Analysis of Pareto Front Approximation for a Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm},\n type = {inproceedings},\n year = {2017},\n pages = {384-389},\n id = {6b995fd5-4d75-3340-9197-f19d6c7829eb},\n created = {2021-11-12T08:31:40.955Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:40.955Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Martins, Marcella Scoczynski Ribeiro and Delgado, Myriam Regattieri and Lüders, Ricardo and Santana, Roberto and Gonçalves, Richard A and de Almeida, Carolina P},\n booktitle = {2017 Brazilian Conference on Intelligent Systems, BRACIS 2017}\n}
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\n \n\n \n \n \n \n \n An extensive analysis of the interaction between missing data types, imputation methods, and supervised classifiers.\n \n \n \n\n\n \n Garciarena, U.; and Santana, R.\n\n\n \n\n\n\n Expert Systems with Applications, 89: 52-65. 2017.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {An extensive analysis of the interaction between missing data types, imputation methods, and supervised classifiers},\n type = {article},\n year = {2017},\n pages = {52-65},\n volume = {89},\n id = {93ed7a46-1ffe-350d-b5b1-6b6d59eece49},\n created = {2021-11-12T08:31:47.619Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:47.619Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Garciarena, Unai and Santana, Roberto},\n journal = {Expert Systems with Applications}\n}
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\n \n\n \n \n \n \n \n Are we generating instances uniformly at random?.\n \n \n \n\n\n \n Ceberio, J.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n In 2017 IEEE Congress on Evolutionary Computation, CEC 2017, pages 1645-1651, 2017. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{\n title = {Are we generating instances uniformly at random?},\n type = {inproceedings},\n year = {2017},\n pages = {1645-1651},\n id = {3f308c38-c6e3-3bff-9f57-6dba8b1174ba},\n created = {2021-11-12T08:31:47.892Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:47.892Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Ceberio, Josu and Mendiburu, Alexander and Lozano, José Antonio},\n booktitle = {2017 IEEE Congress on Evolutionary Computation, CEC 2017}\n}
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\n \n\n \n \n \n \n \n Not all PBILs are the same: Unveiling the different learning mechanisms of PBIL variants.\n \n \n \n\n\n \n de Souza, M., Z.; Santana, R.; Mendiburu, A.; and Pozo, A., T., R.\n\n\n \n\n\n\n Applied Soft Computing, 53: 88-96. 2017.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{\n title = {Not all PBILs are the same: Unveiling the different learning mechanisms of PBIL variants},\n type = {article},\n year = {2017},\n pages = {88-96},\n volume = {53},\n id = {16f11fee-9a9f-3342-8a6b-cb61f329990e},\n created = {2021-11-12T08:31:56.588Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:56.588Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {de Souza, Murilo Zangari and Santana, Roberto and Mendiburu, Alexander and Pozo, Aurora Trinidad Ramirez},\n journal = {Applied Soft Computing}\n}
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\n \n\n \n \n \n \n \n Nature-inspired approaches for distance metric learning in multivariate time series classification.\n \n \n \n\n\n \n Oregi, I.; Ser, J., D.; Pérez, A.; and Lozano, J., A.\n\n\n \n\n\n\n In 2017 IEEE Congress on Evolutionary Computation, CEC 2017, Donostia, San Sebastián, Spain, June 5-8, 2017, pages 1992-1998, 2017. \n \n\n\n\n
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@inproceedings{\n title = {Nature-inspired approaches for distance metric learning in multivariate time series classification},\n type = {inproceedings},\n year = {2017},\n pages = {1992-1998},\n id = {217e7cc0-26c6-35ab-9e66-bfffd675a0b8},\n created = {2021-11-12T08:31:58.815Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:58.815Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Oregi, Izaskun and Ser, Javier Del and Pérez, Aritz and Lozano, José Antonio},\n booktitle = {2017 IEEE Congress on Evolutionary Computation, CEC 2017, Donostia, San Sebastián, Spain, June 5-8, 2017}\n}
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\n  \n 2016\n \n \n (26)\n \n \n
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\n \n\n \n \n \n \n \n \n Semi-supervised Multi-class Classification Problems with Scarcity of Labelled Data: A Theoretical Study.\n \n \n \n \n\n\n \n Ortigosa-Hernández, J.; Inza, I.; and Lozano, J., A.\n\n\n \n\n\n\n IEEE Trans. on Neural Networks and Learning Systems (Early access), 27(12): 2602-2614. 2016.\n \n\n\n\n
\n\n\n\n \n \n \"Semi-supervisedWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{\n title = {Semi-supervised Multi-class Classification Problems with Scarcity of Labelled Data: A Theoretical Study},\n type = {article},\n year = {2016},\n pages = {2602-2614},\n volume = {27},\n websites = {https://doi.org/10.1109/TNNLS.2015.2498525},\n id = {ec521313-91cd-37e3-bcb8-0ec4ee131ca6},\n created = {2021-11-12T08:30:01.882Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:01.882Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Ortigosa-Hernández, Jonathan and Inza, Iñaki and Lozano, José Antonio},\n doi = {10.1109/TNNLS.2015.2498525},\n journal = {IEEE Trans. on Neural Networks and Learning Systems (Early access)},\n number = {12}\n}
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\n \n\n \n \n \n \n \n Efficient approximation of probability distributions with k-order decomposable models.\n \n \n \n\n\n \n Pérez, A.; Inza, I.; and Lozano, J., A.\n\n\n \n\n\n\n Int. J. Approx. Reasoning, 74: 58-87. 2016.\n \n\n\n\n
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@article{\n title = {Efficient approximation of probability distributions with k-order decomposable models},\n type = {article},\n year = {2016},\n pages = {58-87},\n volume = {74},\n id = {63a7c0d9-a037-36ea-ae6d-08c186132470},\n created = {2021-11-12T08:30:07.094Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:07.094Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Pérez, Aritz and Inza, Iñaki and Lozano, José Antonio},\n journal = {Int. J. Approx. Reasoning}\n}
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\n \n\n \n \n \n \n \n PerMallows: An R package for mallows and generalized mallows models.\n \n \n \n\n\n \n Irurozki, E.; Calvo, B.; and Lozano, J., A.\n\n\n \n\n\n\n Journal of Statistical Software, 71. 2016.\n \n\n\n\n
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@article{\n title = {PerMallows: An R package for mallows and generalized mallows models},\n type = {article},\n year = {2016},\n keywords = {Cayley,Generalized Mallows,Hamming,Kendall's τ,Learning,Mallows,Permutation,R,Ranking,Sampling,Ulam},\n volume = {71},\n id = {cd7af9c3-21b1-3874-b2c5-5d3b79b65fa7},\n created = {2021-11-12T08:30:22.359Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:22.359Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {© 2016, American Statistical Association. All rights reserved. In this paper we present the R package PerMallows, which is a complete toolbox to work with permutations, distances and some of the most popular probability models for permutations: Mallows and the Generalized Mallows models. The Mallows model is an exponential location model, considered as analogous to the Gaussian distribution. It is based on the definition of a distance between permutations. The Generalized Mallows model is its best-known extension. The package includes functions for making inference, sampling and learning such distributions. The distances considered in PerMallows are Kendall's τ, Cayley, Hamming and Ulam.},\n bibtype = {article},\n author = {Irurozki, E and Calvo, B and Lozano, J A},\n journal = {Journal of Statistical Software}\n}
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\n © 2016, American Statistical Association. All rights reserved. In this paper we present the R package PerMallows, which is a complete toolbox to work with permutations, distances and some of the most popular probability models for permutations: Mallows and the Generalized Mallows models. The Mallows model is an exponential location model, considered as analogous to the Gaussian distribution. It is based on the definition of a distance between permutations. The Generalized Mallows model is its best-known extension. The package includes functions for making inference, sampling and learning such distributions. The distances considered in PerMallows are Kendall's τ, Cayley, Hamming and Ulam.\n
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\n \n\n \n \n \n \n \n Construct, Merge, Solve & Adapt A new general algorithm for combinatorial optimization.\n \n \n \n\n\n \n Blum, C.; Pinacho, P.; López-Ibáez, M.; and Lozano, J., A.\n\n\n \n\n\n\n Computers & Operations Research, 68: 75-88. 2016.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Construct, Merge, Solve &amp; Adapt A new general algorithm for combinatorial optimization},\n type = {article},\n year = {2016},\n keywords = {isg_ehu,isg_jcr},\n pages = {75-88},\n volume = {68},\n id = {1b5903c1-bc6d-37ed-a225-df0de09bd60e},\n created = {2021-11-12T08:30:28.117Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:28.117Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Blum, Christian and Pinacho, Pedro and López-Ibáez, Manuel and Lozano, José A},\n journal = {Computers & Operations Research}\n}
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\n \n\n \n \n \n \n \n Analyzing the Performance of Allocation Strategies Based on Space-Filling Curves.\n \n \n \n\n\n \n Pascual, J., A.; Lozano, J., A.; and Miguel-Alonso, J.\n\n\n \n\n\n\n In Desai, N.; and Cirne, W., editor(s), Job Scheduling Strategies for Parallel Processing - 19th and 20th International Workshops, JSSPP 2015, Hyderabad, India, May 26, 2015 and JSSPP 2016, Chicago, IL, USA, May 27, 2016, Revised Selected Papers, volume 10353, of Lecture Notes in Computer Science, pages 232-251, 2016. Springer\n \n\n\n\n
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@inproceedings{\n title = {Analyzing the Performance of Allocation Strategies Based on Space-Filling Curves},\n type = {inproceedings},\n year = {2016},\n pages = {232-251},\n volume = {10353},\n publisher = {Springer},\n series = {Lecture Notes in Computer Science},\n id = {5753b222-d5f3-3a06-b0a0-0bccc38c1c6a},\n created = {2021-11-12T08:30:29.510Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:29.510Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Pascual, Jose Antonio and Lozano, José Antonio and Miguel-Alonso, José},\n editor = {Desai, Narayan and Cirne, Walfredo},\n booktitle = {Job Scheduling Strategies for Parallel Processing - 19th and 20th International Workshops, JSSPP 2015, Hyderabad, India, May 26, 2015 and JSSPP 2016, Chicago, IL, USA, May 27, 2016, Revised Selected Papers}\n}
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\n \n\n \n \n \n \n \n Investigating Selection Strategies in Multi-objective Probabilistic Model Based Algorithms.\n \n \n \n\n\n \n Strickler, A.; Junior, O., R., C.; Pozo, A., T., R.; and Santana, R.\n\n\n \n\n\n\n In 5th Brazilian Conference on Intelligent Systems, BRACIS 2016, pages 7-12, 2016. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{\n title = {Investigating Selection Strategies in Multi-objective Probabilistic Model Based Algorithms},\n type = {inproceedings},\n year = {2016},\n pages = {7-12},\n id = {c0167966-9138-394c-bbf9-383c09a0093b},\n created = {2021-11-12T08:30:31.464Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:31.464Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Strickler, Andrei and Junior, Olacir Rodrigues Castro and Pozo, Aurora T R and Santana, Roberto},\n booktitle = {5th Brazilian Conference on Intelligent Systems, BRACIS 2016}\n}
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\n \n\n \n \n \n \n \n A review of message passing algorithms in estimation of distribution algorithms.\n \n \n \n\n\n \n Santana, R.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n Natural Computing, 15(1): 165-180. 2016.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{\n title = {A review of message passing algorithms in estimation of distribution algorithms},\n type = {article},\n year = {2016},\n pages = {165-180},\n volume = {15},\n id = {57bc2d32-cd05-3d74-b8ae-b2755bfb51ab},\n created = {2021-11-12T08:30:32.581Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:32.581Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Santana, Roberto and Mendiburu, Alexander and Lozano, José Antonio},\n journal = {Natural Computing},\n number = {1}\n}
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\n \n\n \n \n \n \n \n Bayesian optimization for parameter tuning in evolutionary algorithms.\n \n \n \n\n\n \n Roman, I.; Ceberio, J.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n In IEEE Congress on Evolutionary Computation, CEC 2016, Vancouver, BC, Canada, July 24-29, 2016, pages 4839-4845, 2016. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{\n title = {Bayesian optimization for parameter tuning in evolutionary algorithms},\n type = {inproceedings},\n year = {2016},\n pages = {4839-4845},\n id = {13e092bf-9a86-3a09-921f-6b5d87729c1d},\n created = {2021-11-12T08:30:45.349Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:45.349Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Roman, Ibai and Ceberio, Josu and Mendiburu, Alexander and Lozano, José Antonio},\n booktitle = {IEEE Congress on Evolutionary Computation, CEC 2016, Vancouver, BC, Canada, July 24-29, 2016}\n}
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\n \n\n \n \n \n \n \n On the Design of Hard mUBQP Instances.\n \n \n \n\n\n \n Zangari, M.; Santana, R.; Mendiburu, A.; and Pozo, A., T., R.\n\n\n \n\n\n\n In Proceedings of the 2016 on Genetic and Evolutionary Computation Conference, pages 421-428, 2016. \n \n\n\n\n
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@inproceedings{\n title = {On the Design of Hard mUBQP Instances},\n type = {inproceedings},\n year = {2016},\n pages = {421-428},\n id = {0244a30a-00e1-3783-aeb7-4fcf4d5a76c2},\n created = {2021-11-12T08:30:46.628Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:46.628Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Zangari, Murilo and Santana, Roberto and Mendiburu, Alexander and Pozo, Aurora T R},\n booktitle = {Proceedings of the 2016 on Genetic and Evolutionary Computation Conference}\n}
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\n \n\n \n \n \n \n \n A Note on the Boltzmann Distribution and the Linear Ordering Problem.\n \n \n \n\n\n \n Ceberio, J.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n In 17th Conference of the Spanish Association for Artificial Intelligence, pages 441-446, 2016. \n \n\n\n\n
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@inproceedings{\n title = {A Note on the Boltzmann Distribution and the Linear Ordering Problem},\n type = {inproceedings},\n year = {2016},\n pages = {441-446},\n id = {6753e4a9-01c6-31f4-915d-9ca531216d87},\n created = {2021-11-12T08:30:54.000Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:54.000Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Ceberio, Josu and Mendiburu, Alexander and Lozano, José Antonio},\n booktitle = {17th Conference of the Spanish Association for Artificial Intelligence}\n}
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\n \n\n \n \n \n \n \n User Adapted Motor-Imaginary Brain-Computer Interface by means of EEG Channel Selection based on Estimation of Distributed Algorithms.\n \n \n \n\n\n \n Astigarraga, A.; Arruti, A.; Muguerza, J.; Santana, R.; Martin, J., I.; and Sierra, B.\n\n\n \n\n\n\n Mathematical Problems in Engineering, (1435321). 2016.\n \n\n\n\n
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@article{\n title = {User Adapted Motor-Imaginary Brain-Computer Interface by means of EEG Channel Selection based on Estimation of Distributed Algorithms},\n type = {article},\n year = {2016},\n keywords = {isg_ehu,isg_jcr},\n id = {d82d5ee5-ce7d-38b0-b9b7-1261f5ac267a},\n created = {2021-11-12T08:31:01.621Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:01.621Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Astigarraga, Aitzol and Arruti, Andoni and Muguerza, Javier and Santana, Roberto and Martin, Jose I and Sierra, Basilio},\n journal = {Mathematical Problems in Engineering},\n number = {1435321}\n}
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\n \n\n \n \n \n \n \n Evolutionary Optimization of Compiler Flag Selection by Learning and Exploiting Flags Interactions.\n \n \n \n\n\n \n Garciarena, U.; and Santana, R.\n\n\n \n\n\n\n In Genetic and Evolutionary Computation Conference, GECCO 2016, Denver, CO, USA, July 20-24, 2016, Companion Material Proceedings, pages 1159-1166, 2016. \n \n\n\n\n
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@inproceedings{\n title = {Evolutionary Optimization of Compiler Flag Selection by Learning and Exploiting Flags Interactions},\n type = {inproceedings},\n year = {2016},\n pages = {1159-1166},\n id = {0724de61-61ff-3eb1-aefe-d3024c057a59},\n created = {2021-11-12T08:31:03.790Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:03.790Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Garciarena, Unai and Santana, Roberto},\n booktitle = {Genetic and Evolutionary Computation Conference, GECCO 2016, Denver, CO, USA, July 20-24, 2016, Companion Material Proceedings}\n}
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\n \n\n \n \n \n \n \n Evolutionary Approaches to Optimization Problems in Chimera Topologies.\n \n \n \n\n\n \n Santana, R.; Zhu, Z.; and Katzgraber, H., G.\n\n\n \n\n\n\n CoRR, abs/1608.0. 2016.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{\n title = {Evolutionary Approaches to Optimization Problems in Chimera Topologies},\n type = {article},\n year = {2016},\n volume = {abs/1608.0},\n id = {1652c617-0de8-3909-8063-430eb6dcf410},\n created = {2021-11-12T08:31:10.660Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:10.660Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Santana, Roberto and Zhu, Zheng and Katzgraber, Helmut G},\n journal = {CoRR}\n}
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\n \n\n \n \n \n \n \n Maximal nonlinearity in balanced boolean functions with even number of inputs, revisited.\n \n \n \n\n\n \n Picek, S.; Santana, R.; and Jakobovic, D.\n\n\n \n\n\n\n In IEEE Congress on Evolutionary Computation, pages 3222-3229, 2016. \n \n\n\n\n
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@inproceedings{\n title = {Maximal nonlinearity in balanced boolean functions with even number of inputs, revisited},\n type = {inproceedings},\n year = {2016},\n pages = {3222-3229},\n id = {263bbbae-6bdc-355e-a038-42ec01644fd6},\n created = {2021-11-12T08:31:17.987Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:17.987Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Picek, Stjepan and Santana, Roberto and Jakobovic, Domagoj},\n booktitle = {IEEE Congress on Evolutionary Computation}\n}
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\n \n\n \n \n \n \n \n An efficient K-means algorithm for Massive Data.\n \n \n \n\n\n \n Capó, M.; Pérez, A.; and Lozano, J., A.\n\n\n \n\n\n\n CoRR, abs/1605.0. 2016.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{\n title = {An efficient K-means algorithm for Massive Data},\n type = {article},\n year = {2016},\n volume = {abs/1605.0},\n id = {ed2d6ee5-762d-3002-be7b-dcb51ef14d75},\n created = {2021-11-12T08:31:19.067Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:19.067Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Capó, Marco and Pérez, Aritz and Lozano, José Antonio},\n journal = {CoRR}\n}
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\n \n\n \n \n \n \n \n Kernel density estimation in accelerators - Implementation and performance evaluation.\n \n \n \n\n\n \n Lopez-Novoa, U.; Mendiburu, A.; and Miguel-Alonso, J.\n\n\n \n\n\n\n The Journal of Supercomputing, 72(2): 545-566. 2016.\n \n\n\n\n
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@article{\n title = {Kernel density estimation in accelerators - Implementation and performance evaluation},\n type = {article},\n year = {2016},\n keywords = {isg_ehu,isg_jcr},\n pages = {545-566},\n volume = {72},\n id = {a94ff206-d9d6-3efc-b6c1-29f0bd899749},\n created = {2021-11-12T08:31:25.974Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:25.974Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Lopez-Novoa, Unai and Mendiburu, Alexander and Miguel-Alonso, José},\n journal = {The Journal of Supercomputing},\n number = {2}\n}
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\n \n\n \n \n \n \n \n scmamp: Statistical Comparison of Multiple Algorithms in Multiple Problems.\n \n \n \n\n\n \n Calvo, B.; and Santafé, G.\n\n\n \n\n\n\n The R Journal, 8(1): 248-256. 2016.\n \n\n\n\n
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@article{\n title = {scmamp: Statistical Comparison of Multiple Algorithms in Multiple Problems},\n type = {article},\n year = {2016},\n keywords = {isg_ehu,isg_jcr},\n pages = {248-256},\n volume = {8},\n id = {aa57f809-bf33-3e56-9981-f10c13cd1d04},\n created = {2021-11-12T08:31:34.209Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:34.209Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Calvo, Borja and Santafé, Guzmán},\n journal = {The R Journal},\n number = {1}\n}
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\n \n\n \n \n \n \n \n Incremental learning algorithms and applications.\n \n \n \n\n\n \n Gepperth, A.; and Hammer, B.\n\n\n \n\n\n\n European symposium on artificial neural networks (esann). 2016.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Incremental learning algorithms and applications},\n type = {article},\n year = {2016},\n id = {c5427c44-6260-3d1d-8bc9-85b58841ce46},\n created = {2021-11-12T08:31:35.324Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:35.324Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Gepperth, Alexander and Hammer, Barbara},\n journal = {European symposium on artificial neural networks (esann)}\n}
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\n \n\n \n \n \n \n \n Distance Measures for Time Series in R: The TSdist Package.\n \n \n \n\n\n \n Mori, U.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n The R Journal, 8(2): 451-459. 2016.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Distance Measures for Time Series in R: The TSdist Package},\n type = {article},\n year = {2016},\n pages = {451-459},\n volume = {8},\n id = {70277e45-7351-3944-ad2e-6a5f3de6300a},\n created = {2021-11-12T08:31:42.927Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:42.927Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Mori, Usue and Mendiburu, Alexander and Lozano, Jose A},\n journal = {The R Journal},\n number = {2}\n}
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\n \n\n \n \n \n \n \n HMOBEDA: Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm.\n \n \n \n\n\n \n Martins, M., S., R.; de Biase da Silva Delgado, M., R.; Santana, R.; Lüders, R.; Gonçalves, R., A.; and de Almeida, C., P.\n\n\n \n\n\n\n In Proceedings of the 2016 on Genetic and Evolutionary Computation Conference, Denver, CO, USA, July 20 - 24, 2016, pages 357-364, 2016. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {HMOBEDA: Hybrid Multi-objective Bayesian Estimation of Distribution Algorithm},\n type = {inproceedings},\n year = {2016},\n pages = {357-364},\n id = {34209b89-b345-38c4-91e9-83a40b6265c0},\n created = {2021-11-12T08:31:44.096Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:44.096Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Martins, Marcella S R and de Biase da Silva Delgado, Myriam Regattieri and Santana, Roberto and Lüders, Ricardo and Gonçalves, Richard Aderbal and de Almeida, Carolina Paula},\n booktitle = {Proceedings of the 2016 on Genetic and Evolutionary Computation Conference, Denver, CO, USA, July 20 - 24, 2016}\n}
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\n \n\n \n \n \n \n \n Estimating Attraction Basin Sizes.\n \n \n \n\n\n \n Hernando, L.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n In Advances in Artificial Intelligence - 17th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2016, Salamanca, Spain, September 14-16, 2016. Proceedings, pages 458-467, 2016. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Estimating Attraction Basin Sizes},\n type = {inproceedings},\n year = {2016},\n pages = {458-467},\n id = {f20802cd-dbcd-32fa-9cf9-f2764bed3c01},\n created = {2021-11-12T08:31:44.372Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:44.372Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Hernando, Leticia and Mendiburu, Alexander and Lozano, José Antonio},\n booktitle = {Advances in Artificial Intelligence - 17th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2016, Salamanca, Spain, September 14-16, 2016. Proceedings}\n}
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\n \n\n \n \n \n \n \n Vine copula classifiers for the mind reading problem.\n \n \n \n\n\n \n Carrera, D.; Santana, R.; and Lozano, J., A.\n\n\n \n\n\n\n Progress in AI, 5(4): 289-305. 2016.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Vine copula classifiers for the mind reading problem},\n type = {article},\n year = {2016},\n pages = {289-305},\n volume = {5},\n id = {b7fa9e42-ff35-3e2a-9362-740fe0c7650d},\n created = {2021-11-12T08:31:50.964Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:50.964Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Carrera, Diana and Santana, Roberto and Lozano, José Antonio},\n journal = {Progress in AI},\n number = {4}\n}
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\n \n\n \n \n \n \n \n A tunable generator of instances of permutation-based combinatorial optimization problems.\n \n \n \n\n\n \n Hernando, L.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n IEEE Transactions on Evolutionary Computation, 20(2): 165-179. 2016.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {A tunable generator of instances of permutation-based combinatorial optimization problems},\n type = {article},\n year = {2016},\n keywords = {isg_ehu,isg_jcr},\n pages = {165-179},\n volume = {20},\n id = {893ca66f-fa4a-3f60-85d4-4a8bb23de1c0},\n created = {2021-11-12T08:31:51.830Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:51.830Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Hernando, Leticia and Mendiburu, Alexander and Lozano, Jose A},\n journal = {IEEE Transactions on Evolutionary Computation},\n number = {2}\n}
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\n \n\n \n \n \n \n \n Weak supervision and other non-standard classification problems: A taxonomy.\n \n \n \n\n\n \n Hernández-González, J.; Inza, I.; and Lozano, J., A.\n\n\n \n\n\n\n Pattern Recognition Letters. 2016.\n \n\n\n\n
\n\n\n\n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Weak supervision and other non-standard classification problems: A taxonomy},\n type = {article},\n year = {2016},\n keywords = {Degrees of supervision,Partially supervised classification,Weakly supervised classification},\n id = {b10466a3-a885-3d1d-8dda-110c33b59e55},\n created = {2021-11-12T08:31:59.412Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:59.412Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {In recent years, different researchers in the machine learning community have presented new classification frameworks which go beyond the standard supervised classification in different aspects. Specifically, a wide spectrum of novel frameworks that use partially labeled data in the construction of classifiers has been studied. With the objective of drawing up a description of the state-of-the-art, three identifying characteristics of these novel frameworks have been considered: (1) the relationship between instances and labels of a problem, which may be beyond the one-instance one-label standard, (2) the possible provision of partial class information for the training examples, and (3) the possible provision of partial class information also for the examples in the prediction stage. These three ideas have been formulated as axes of a comprehensive taxonomy that organizes the state-of-the-art. The proposed organization allows us both to understand similarities/differences among the different classification problems already presented in the literature as well as to discover unexplored frameworks that might be seen as further challenges and research opportunities. A representative set of state-of-the-art problems has been used to illustrate the novel taxonomy and support the discussion.},\n bibtype = {article},\n author = {Hernández-González, Jerónimo and Inza, Iñaki and Lozano, Jose A},\n doi = {10.1016/j.patrec.2015.10.008},\n journal = {Pattern Recognition Letters}\n}
\n
\n\n\n
\n In recent years, different researchers in the machine learning community have presented new classification frameworks which go beyond the standard supervised classification in different aspects. Specifically, a wide spectrum of novel frameworks that use partially labeled data in the construction of classifiers has been studied. With the objective of drawing up a description of the state-of-the-art, three identifying characteristics of these novel frameworks have been considered: (1) the relationship between instances and labels of a problem, which may be beyond the one-instance one-label standard, (2) the possible provision of partial class information for the training examples, and (3) the possible provision of partial class information also for the examples in the prediction stage. These three ideas have been formulated as axes of a comprehensive taxonomy that organizes the state-of-the-art. The proposed organization allows us both to understand similarities/differences among the different classification problems already presented in the literature as well as to discover unexplored frameworks that might be seen as further challenges and research opportunities. A representative set of state-of-the-art problems has been used to illustrate the novel taxonomy and support the discussion.\n
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\n \n\n \n \n \n \n \n \n Similarity Measure Selection for Clustering Time Series Databases.\n \n \n \n \n\n\n \n Mori, U.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n IEEE Transactions on Knowledge and Data Engineering, 28(1): 181-195. 2016.\n \n\n\n\n
\n\n\n\n \n \n \"SimilarityWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Similarity Measure Selection for Clustering Time Series Databases},\n type = {article},\n year = {2016},\n keywords = {isg_ehu,isg_jcr},\n pages = {181-195},\n volume = {28},\n websites = {http://dx.doi.org/10.1109/TKDE.2015.2462369},\n id = {859318b4-229e-32ce-8853-30f0653df06e},\n created = {2021-11-12T08:32:01.214Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:01.214Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Mori, U and Mendiburu, A and Lozano, J A},\n doi = {10.1109/TKDE.2015.2462369},\n journal = {IEEE Transactions on Knowledge and Data Engineering},\n number = {1}\n}
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\n \n\n \n \n \n \n \n \n C-Multi: A competent multi-swarm approach for many-objective problems.\n \n \n \n \n\n\n \n Castro, O., R.; Santana, R.; and Pozo, A.\n\n\n \n\n\n\n Neurocomputing, 180: 68-78. 2016.\n \n\n\n\n
\n\n\n\n \n \n \"C-Multi:Website\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {C-Multi: A competent multi-swarm approach for many-objective problems},\n type = {article},\n year = {2016},\n keywords = {Competent algorithm,Estimation of distribution algorithm,Many-objective,Particle swarm optimization},\n pages = {68-78},\n volume = {180},\n websites = {https://www.sciencedirect.com/science/article/pii/S0925231215016215},\n id = {2b9ee43d-fa21-3209-bf20-750950b1d59a},\n created = {2021-11-12T08:32:11.491Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:11.491Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {One of the major research topics in the evolutionary multi-objective community is handling a large number of objectives also known as many-objective optimization problems (MaOPs). Most existing methodologies have demonstrated success for problems with two and three objectives but face significant challenges in many-objective optimization. To tackle these challenges, a hybrid multi-swarm algorithm called C-Multi was proposed in a previous work. The project of C-Multi is based on two phases; the first uses a unique particle swarm optimization (PSO) algorithm to discover different regions of the Pareto front. The second phase uses multiple swarms to specialize on a dedicate part. On each sub-swarm, an estimation of distribution algorithm (EDA) is used to focus on convergence to its allocated region. In this study, the influence of two critical components of C-Multi, the archiving method and the number of swarms, is investigated by empirical analysis. As a result of this investigation, an improved variant of C-Multi is obtained, and its performance is compared to I-Multi, a multi-swarm algorithm that has a similar approach but does not use EDAs. Empirical results fully demonstrate the superiority of our proposed method on almost all considered test instances.},\n bibtype = {article},\n author = {Castro, Olacir R and Santana, Roberto and Pozo, Aurora},\n doi = {https://doi.org/10.1016/j.neucom.2015.06.097},\n journal = {Neurocomputing}\n}
\n
\n\n\n
\n One of the major research topics in the evolutionary multi-objective community is handling a large number of objectives also known as many-objective optimization problems (MaOPs). Most existing methodologies have demonstrated success for problems with two and three objectives but face significant challenges in many-objective optimization. To tackle these challenges, a hybrid multi-swarm algorithm called C-Multi was proposed in a previous work. The project of C-Multi is based on two phases; the first uses a unique particle swarm optimization (PSO) algorithm to discover different regions of the Pareto front. The second phase uses multiple swarms to specialize on a dedicate part. On each sub-swarm, an estimation of distribution algorithm (EDA) is used to focus on convergence to its allocated region. In this study, the influence of two critical components of C-Multi, the archiving method and the number of swarms, is investigated by empirical analysis. As a result of this investigation, an improved variant of C-Multi is obtained, and its performance is compared to I-Multi, a multi-swarm algorithm that has a similar approach but does not use EDAs. Empirical results fully demonstrate the superiority of our proposed method on almost all considered test instances.\n
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\n  \n 2015\n \n \n (44)\n \n \n
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\n \n\n \n \n \n \n \n Estimation of the distribution algorithm with a stochastic local search for uncertain capacitated arc routing problems.\n \n \n \n\n\n \n Wang, J.; Tang, K.; Lozano, J., A.; and Yao, X.\n\n\n \n\n\n\n IEEE Transactions on Evolutionary Computation, 20(1): 96-109. 2015.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Estimation of the distribution algorithm with a stochastic local search for uncertain capacitated arc routing problems},\n type = {article},\n year = {2015},\n keywords = {isg_ehu,isg_jcr},\n pages = {96-109},\n volume = {20},\n publisher = {IEEE},\n id = {4dfbc86c-6e15-3cf1-9df4-a7b8bb146bf8},\n created = {2021-11-12T08:30:01.568Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:01.568Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Wang, Juan and Tang, Ke and Lozano, Jose A and Yao, Xin},\n journal = {IEEE Transactions on Evolutionary Computation},\n number = {1}\n}
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\n \n\n \n \n \n \n \n Fighting the Symmetries: The Structure of Cryptographic Boolean Function Spaces.\n \n \n \n\n\n \n Picek, S.; McKay, R., I.; Santana, R.; and Gedeon, T., D.\n\n\n \n\n\n\n In Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference, pages 457-464, 2015. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@inproceedings{\n title = {Fighting the Symmetries: The Structure of Cryptographic Boolean Function Spaces},\n type = {inproceedings},\n year = {2015},\n keywords = {isg_ehu},\n pages = {457-464},\n id = {f9c642b9-158f-3fd3-80f5-81aded937da8},\n created = {2021-11-12T08:30:08.118Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:08.118Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Picek, S and McKay, R I and Santana, R and Gedeon, T D},\n booktitle = {Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference}\n}
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\n \n\n \n \n \n \n \n Contributions to learning Bayesian network models from weakly supervised data: Application to Assisted Reproductive Technologies and Software Defect Classification.\n \n \n \n\n\n \n Hernández-González, J.\n\n\n \n\n\n\n Ph.D. Thesis, 2015.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@phdthesis{\n title = {Contributions to learning Bayesian network models from weakly supervised data: Application to Assisted Reproductive Technologies and Software Defect Classification},\n type = {phdthesis},\n year = {2015},\n institution = {University of the Basque Country},\n id = {afe87d7b-ddcc-3734-ab54-1705827914cc},\n created = {2021-11-12T08:30:16.536Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:16.536Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {phdthesis},\n private_publication = {false},\n bibtype = {phdthesis},\n author = {Hernández-González, Jerónimo}\n}
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\n \n\n \n \n \n \n \n Modeling the availability of Cassandra.\n \n \n \n\n\n \n Pérez-Miguel, C.; Mendiburu, A.; and Miguel-Alonso, J.\n\n\n \n\n\n\n J. Parallel Distrib. Comput., 86: 29-44. 2015.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Modeling the availability of Cassandra},\n type = {article},\n year = {2015},\n keywords = {isg_ehu,isg_jcr},\n pages = {29-44},\n volume = {86},\n id = {351381b0-8d9d-3148-acc0-0f5cd0fab27d},\n created = {2021-11-12T08:30:16.796Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:16.796Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Pérez-Miguel, Carlos and Mendiburu, Alexander and Miguel-Alonso, José},\n journal = {J. Parallel Distrib. Comput.}\n}
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\n \n\n \n \n \n \n \n A Comparison of Estimation of Distribution Algorithms for the Linear Ordering Problem.\n \n \n \n\n\n \n Ceberio, J.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n In X Congreso Español de Metaheuristicas, Algoritmos Evolutivos y Bioinspirados - MAEB 2015, pages 97-102, 2015. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {A Comparison of Estimation of Distribution Algorithms for the Linear Ordering Problem},\n type = {inproceedings},\n year = {2015},\n keywords = {isg_ehu},\n pages = {97-102},\n id = {651be137-5589-3d67-ae1d-d8663fe59bf7},\n created = {2021-11-12T08:30:22.079Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:22.079Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Ceberio, Josu and Mendiburu, Alexander and Lozano, Jose A},\n booktitle = {X Congreso Español de Metaheuristicas, Algoritmos Evolutivos y Bioinspirados - MAEB 2015}\n}
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\n \n\n \n \n \n \n \n MOEA/D-GM: Using probabilistic graphical models in MOEA/D for solving combinatorial optimization problems.\n \n \n \n\n\n \n de Souza, M., Z.; Santana, R.; Pozo, A., T.; and Mendiburu, A.\n\n\n \n\n\n\n CoRR, abs/1511.0. 2015.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {MOEA/D-GM: Using probabilistic graphical models in MOEA/D for solving combinatorial optimization problems},\n type = {article},\n year = {2015},\n keywords = {isg_ehu,isg_jcr},\n volume = {abs/1511.0},\n id = {9206683f-2735-3acd-825e-d9f7f6318938},\n created = {2021-11-12T08:30:23.436Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:23.436Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {de Souza, Murilo Z and Santana, Roberto and Pozo, Aurora T and Mendiburu, Alexander},\n journal = {CoRR}\n}
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\n \n\n \n \n \n \n \n Multidimensional learning from crowds: Usefulness and application of expertise detection.\n \n \n \n\n\n \n Hernández-González, J.; Inza, I.; and Lozano, J., A.\n\n\n \n\n\n\n International Journal of Intelligent Systems, 30(3): 326-354. 2015.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Multidimensional learning from crowds: Usefulness and application of expertise detection},\n type = {article},\n year = {2015},\n keywords = {isg_ehu,isg_jcr},\n pages = {326-354},\n volume = {30},\n id = {289328d5-dbd9-348e-9528-ff3f10ed33c4},\n created = {2021-11-12T08:30:23.989Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:23.989Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Hernández-González, Jerónimo and Inza, Iñaki and Lozano, Jose A},\n journal = {International Journal of Intelligent Systems},\n number = {3}\n}
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\n \n\n \n \n \n \n \n An Artificial Bioindicator System for Network Intrusion Detection.\n \n \n \n\n\n \n Blum, C.; Lozano, J., A.; and Davidson, P., P.\n\n\n \n\n\n\n Artif. Life, 21(2): 93-118. 2015.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {An Artificial Bioindicator System for Network Intrusion Detection},\n type = {article},\n year = {2015},\n keywords = {isg_ehu,isg_jcr},\n pages = {93-118},\n volume = {21},\n publisher = {MIT Press},\n id = {8a357386-2ee6-3fbf-98b6-f58cd15556b3},\n created = {2021-11-12T08:30:32.299Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:32.299Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Blum, Christian and Lozano, José A and Davidson, Pedro P},\n journal = {Artif. Life},\n number = {2}\n}
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\n \n\n \n \n \n \n \n Contributions to the Efficient Use of General Purpose Coprocessors: Kernel Density Estimation as Case Study.\n \n \n \n\n\n \n Lopez-Novoa, U.\n\n\n \n\n\n\n Ph.D. Thesis, 2015.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@phdthesis{\n title = {Contributions to the Efficient Use of General Purpose Coprocessors: Kernel Density Estimation as Case Study},\n type = {phdthesis},\n year = {2015},\n institution = {University of the Basque Country},\n id = {d442df34-ac08-3a89-b93a-7cbb29b6d774},\n created = {2021-11-12T08:30:32.899Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:32.899Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {phdthesis},\n private_publication = {false},\n bibtype = {phdthesis},\n author = {Lopez-Novoa, Unai}\n}
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\n \n\n \n \n \n \n \n Gene-Gene Interactions Detection Using a Two-stage Model.\n \n \n \n\n\n \n Wang, Z.; Sul, J., H.; Snir, S.; Lozano, J., A.; and Eskin, E.\n\n\n \n\n\n\n Journal of Computational Biology, 22(6): 563-576. 2015.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Gene-Gene Interactions Detection Using a Two-stage Model},\n type = {article},\n year = {2015},\n keywords = {isg_ehu,isg_jcr},\n pages = {563-576},\n volume = {22},\n publisher = {Mary Ann Liebert, Inc. 140 Huguenot Street, 3rd Floor New Rochelle, NY 10801 USA},\n id = {0568a6b1-5d16-3d31-acf4-7ea141726f91},\n created = {2021-11-12T08:30:38.889Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:38.889Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Wang, Zhanyong and Sul, Jae H and Snir, Sagi and Lozano, Jose A and Eskin, Eleazar},\n journal = {Journal of Computational Biology},\n number = {6}\n}
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\n \n\n \n \n \n \n \n Locality-aware policies to improve job scheduling on 3D tori.\n \n \n \n\n\n \n Pascual, J., A.; Miguel-Alonso, J.; and Lozano, J., A.\n\n\n \n\n\n\n The Journal of Supercomputing, 71(3): 966-994. 2015.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Locality-aware policies to improve job scheduling on 3D tori},\n type = {article},\n year = {2015},\n keywords = {isg_ehu,isg_jcr},\n pages = {966-994},\n volume = {71},\n id = {c099c7f2-68d8-3d9d-a171-7b16080bb4cc},\n created = {2021-11-12T08:30:39.459Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:39.459Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Pascual, Jose A and Miguel-Alonso, José and Lozano, Jose A},\n journal = {The Journal of Supercomputing},\n number = {3}\n}
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\n \n\n \n \n \n \n \n A Sparse Spectral Clustering Framework via Multi-Objective Evolutionary Algorithm.\n \n \n \n\n\n \n Luo, J.; Jiao, L.; and Lozano, J.\n\n\n \n\n\n\n IEEE Transactions on Evolutionary Computation, PP(99): 1. 2015.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {A Sparse Spectral Clustering Framework via Multi-Objective Evolutionary Algorithm},\n type = {article},\n year = {2015},\n keywords = {isg_ehu,isg_jcr},\n pages = {1},\n volume = {PP},\n id = {0f6e1236-c346-31aa-9c55-a268184766fc},\n created = {2021-11-12T08:30:40.648Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:40.648Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Luo, J and Jiao, L and Lozano, J},\n journal = {IEEE Transactions on Evolutionary Computation},\n number = {99}\n}
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\n \n\n \n \n \n \n \n Early classification of time series from a cost minimization point of view.\n \n \n \n\n\n \n Mori, U.; undefined Dasgupta; and Lozano, J., A.\n\n\n \n\n\n\n In NIPS time series workshop, of NIPS 2015, 2015. \n \n\n\n\n
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@inproceedings{\n title = {Early classification of time series from a cost minimization point of view.},\n type = {inproceedings},\n year = {2015},\n keywords = {isg_ehu},\n series = {NIPS 2015},\n id = {11107dac-9c95-304a-b07e-b19909be43ac},\n created = {2021-11-12T08:30:46.893Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:46.893Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Mori, U and undefined Dasgupta, undefined and Lozano, J A},\n booktitle = {NIPS time series workshop}\n}
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\n \n\n \n \n \n \n \n Evolving MNK-landscapes with structural constraints.\n \n \n \n\n\n \n Santana, R.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n In Proceedings of the IEEE Congress on Evolutionary Computation CEC 2015, pages 1364-1371, 2015. IEEE press\n \n\n\n\n
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@inproceedings{\n title = {Evolving MNK-landscapes with structural constraints},\n type = {inproceedings},\n year = {2015},\n keywords = {isg_ehu},\n pages = {1364-1371},\n publisher = {IEEE press},\n id = {a47866d2-b1ac-3418-a46b-5900680e8fb0},\n created = {2021-11-12T08:30:47.742Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:47.742Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Santana, R and Mendiburu, A and Lozano, J A},\n booktitle = {Proceedings of the IEEE Congress on Evolutionary Computation CEC 2015}\n}
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\n \n\n \n \n \n \n \n Kernels of Mallows Models for Solving Permutation-based Problems.\n \n \n \n\n\n \n Ceberio, J.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n In In Proceedings of 2015 Genetic and Evolutionary Computation Conference (GECCO-2015), pages 505-512, 2015. \n \n\n\n\n
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@inproceedings{\n title = {Kernels of Mallows Models for Solving Permutation-based Problems},\n type = {inproceedings},\n year = {2015},\n keywords = {isg_ehu},\n pages = {505-512},\n id = {f49e557f-5cc8-3ba7-99dc-f2996ef86c01},\n created = {2021-11-12T08:30:51.506Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:51.506Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Ceberio, Josu and Mendiburu, Alexander and Lozano, Jose A},\n booktitle = {In Proceedings of 2015 Genetic and Evolutionary Computation Conference (GECCO-2015)}\n}
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\n \n\n \n \n \n \n \n Competition-based failure-aware scheduling for High-Throughput Computing systems on peer-to-peer networks.\n \n \n \n\n\n \n Pérez-Miguel, C.; Mendiburu, A.; and Miguel-Alonso, J.\n\n\n \n\n\n\n Cluster Computing, 18(3): 1229-1249. 2015.\n \n\n\n\n
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@article{\n title = {Competition-based failure-aware scheduling for High-Throughput Computing systems on peer-to-peer networks},\n type = {article},\n year = {2015},\n keywords = {isg_ehu,isg_jcr},\n pages = {1229-1249},\n volume = {18},\n id = {35c08ef3-4d46-3bf9-9c9a-da52ad2cf767},\n created = {2021-11-12T08:30:56.141Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:56.141Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Pérez-Miguel, Carlos and Mendiburu, Alexander and Miguel-Alonso, José},\n journal = {Cluster Computing},\n number = {3}\n}
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\n \n\n \n \n \n \n \n Comprehensive characterization of the behaviors of estimation of distribution algorithms.\n \n \n \n\n\n \n Echegoyen, C.; Santana, R.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n Theor. Comput. Sci., 598: 64-86. 2015.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Comprehensive characterization of the behaviors of estimation of distribution algorithms},\n type = {article},\n year = {2015},\n keywords = {isg_ehu,isg_jcr},\n pages = {64-86},\n volume = {598},\n id = {62356c31-78a1-3707-9a24-144dcc5c407b},\n created = {2021-11-12T08:30:56.396Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:56.396Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Echegoyen, Carlos and Santana, Roberto and Mendiburu, Alexander and Lozano, José A},\n journal = {Theor. Comput. Sci.}\n}
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\n \n\n \n \n \n \n \n A matheuristic for the minimum weight rooted arborescence problem.\n \n \n \n\n\n \n Blum, C.; and Calvo, B.\n\n\n \n\n\n\n Journal of Heuristics, 21(4): 479-499. 2015.\n \n\n\n\n
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@article{\n title = {A matheuristic for the minimum weight rooted arborescence problem},\n type = {article},\n year = {2015},\n keywords = {isg_ehu,isg_jcr},\n pages = {479-499},\n volume = {21},\n publisher = {Springer US},\n id = {0157ad46-6749-3820-bb59-46486a2b7c99},\n created = {2021-11-12T08:31:02.751Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:02.751Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Blum, Christian and Calvo, Borja},\n journal = {Journal of Heuristics},\n number = {4}\n}
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\n \n\n \n \n \n \n \n Towards a Greener Cloud Infrastructure Management using Optimized Placement Policies.\n \n \n \n\n\n \n Pascual, J., A.; Lorido-Botrán, T.; Miguel-Alonso, J.; and Lozano, J., A.\n\n\n \n\n\n\n Journal of Grid Computing, Special Issue, 13(3): 375-389. 2015.\n \n\n\n\n
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@article{\n title = {Towards a Greener Cloud Infrastructure Management using Optimized Placement Policies},\n type = {article},\n year = {2015},\n keywords = {isg_ehu,isg_jcr},\n pages = {375-389},\n volume = {13},\n id = {0de9d2be-7ea0-3860-a9a9-69b92faff6d0},\n created = {2021-11-12T08:31:04.070Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:04.070Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Pascual, Jose A and Lorido-Botrán, Tania and Miguel-Alonso, José and Lozano, Jose A},\n journal = {Journal of Grid Computing, Special Issue},\n number = {3}\n}
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\n \n\n \n \n \n \n \n Algoritmos multiobjetivos aplicados ao problema de predicao de estruturas proteinas.\n \n \n \n\n\n \n Da-Fontoura, V., D.; Lima, R.; Pozo, A.; and Santana., R.\n\n\n \n\n\n\n In Memorias del 12 Congresso Brasileiro de Inteligencia Computacional (CBIC 2015), 2015. \n \n\n\n\n
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@inproceedings{\n title = {Algoritmos multiobjetivos aplicados ao problema de predicao de estruturas proteinas},\n type = {inproceedings},\n year = {2015},\n id = {f011c934-8c8f-3f7a-9785-b0106a8746c4},\n created = {2021-11-12T08:31:04.619Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:04.619Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Da-Fontoura, V D and Lima, R and Pozo, A and Santana., R},\n booktitle = {Memorias del 12 Congresso Brasileiro de Inteligencia Computacional (CBIC 2015)}\n}
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\n \n\n \n \n \n \n \n Contributions to High-Throughput Computing Based on the Peer-to-Peer Paradigm.\n \n \n \n\n\n \n Pérez, C.\n\n\n \n\n\n\n Ph.D. Thesis, 2015.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@phdthesis{\n title = {Contributions to High-Throughput Computing Based on the Peer-to-Peer Paradigm},\n type = {phdthesis},\n year = {2015},\n institution = {University of the Basque Country},\n id = {a29d7a4b-002a-385f-8177-e8c1726c4531},\n created = {2021-11-12T08:31:07.042Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:07.042Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {phdthesis},\n private_publication = {false},\n bibtype = {phdthesis},\n author = {Pérez, Carlos}\n}
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\n \n\n \n \n \n \n \n A Review of Distances for the Mallows and Generalized Mallows Estimation of Distribution Algorithms.\n \n \n \n\n\n \n Ceberio, J.; Irurozki, E.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n Computational Optimization and Applications, 62(2): 545-564. 2015.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {A Review of Distances for the Mallows and Generalized Mallows Estimation of Distribution Algorithms},\n type = {article},\n year = {2015},\n keywords = {isg_ehu,isg_jcr},\n pages = {545-564},\n volume = {62},\n id = {b4f7709f-c8b3-3286-93e2-d610957d6d9c},\n created = {2021-11-12T08:31:13.459Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:13.459Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Ceberio, Josu and Irurozki, Ekhine and Mendiburu, Alexander and Lozano, Jose A},\n journal = {Computational Optimization and Applications},\n number = {2}\n}
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\n \n\n \n \n \n \n \n Mathematical programming strategies for solving the minimum common string partition problem.\n \n \n \n\n\n \n Blum, C.; Lozano, J., A.; and Pinacho, P.\n\n\n \n\n\n\n European Journal of Operational Research, 242(3): 769-777. 2015.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Mathematical programming strategies for solving the minimum common string partition problem},\n type = {article},\n year = {2015},\n keywords = {isg_ehu,isg_jcr},\n pages = {769-777},\n volume = {242},\n id = {f2d8a9bf-5261-3a20-8a3e-d55b286e5dac},\n created = {2021-11-12T08:31:13.745Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:13.745Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Blum, Christian and Lozano, Jose A. and Pinacho, Pedro},\n journal = {European Journal of Operational Research},\n number = {3}\n}
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\n \n\n \n \n \n \n \n Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species.\n \n \n \n\n\n \n Fernandes, J., A.; Irigoien, X.; Lozano, J., A.; Inza, I.; Goikoetxea, N.; and Pérez, A.\n\n\n \n\n\n\n Ecological Informatics, 25: 35-42. 2015.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species},\n type = {article},\n year = {2015},\n keywords = {isg_ehu,isg_jcr},\n pages = {35-42},\n volume = {25},\n id = {e3896595-1b2b-3986-8bfe-7324ca5e5fb8},\n created = {2021-11-12T08:31:18.795Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:18.795Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Fernandes, Jose A and Irigoien, Xabier and Lozano, Jose A and Inza, Iñaki and Goikoetxea, Nerea and Pérez, Aritz},\n journal = {Ecological Informatics}\n}
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\n \n\n \n \n \n \n \n Introducing Mixtures of Generalized Mallows in Estimation of Distribution Algorithms.\n \n \n \n\n\n \n Santamaria, J.; Ceberio, J.; Santana, R.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n In X Congreso Español de Metaheuristicas, Algoritmos Evolutivos y Bioinspirados - MAEB 2015, pages 19-25, 2015. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@inproceedings{\n title = {Introducing Mixtures of Generalized Mallows in Estimation of Distribution Algorithms},\n type = {inproceedings},\n year = {2015},\n keywords = {isg_ehu},\n pages = {19-25},\n id = {dad44212-59b8-3430-b1aa-c63849107136},\n created = {2021-11-12T08:31:19.617Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:19.617Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Santamaria, J and Ceberio, J and Santana, R and Mendiburu, A and Lozano, J A},\n booktitle = {X Congreso Español de Metaheuristicas, Algoritmos Evolutivos y Bioinspirados - MAEB 2015}\n}
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\n \n\n \n \n \n \n \n Contributions to time series data mining departing from the problem of road travel time modeling.\n \n \n \n\n\n \n Mori, U.\n\n\n \n\n\n\n Ph.D. Thesis, 2015.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@phdthesis{\n title = {Contributions to time series data mining departing from the problem of road travel time modeling},\n type = {phdthesis},\n year = {2015},\n institution = {University of the Basque Country},\n id = {52178f1e-53bc-3b6c-aa66-666abcf4f419},\n created = {2021-11-12T08:31:20.175Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:20.175Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {phdthesis},\n private_publication = {false},\n bibtype = {phdthesis},\n author = {Mori, Usue}\n}
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\n \n\n \n \n \n \n \n Computing factorized approximations of Pareto-fronts using mNM-landscapes and Boltzmann distributions.\n \n \n \n\n\n \n Santana, R.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n CoRR, abs/1512.0. 2015.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Computing factorized approximations of Pareto-fronts using mNM-landscapes and Boltzmann distributions},\n type = {article},\n year = {2015},\n keywords = {isg_ehu,isg_jcr},\n volume = {abs/1512.0},\n id = {27f0fb9b-402d-3530-ab0f-9ab43b149f8c},\n created = {2021-11-12T08:31:20.453Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:20.453Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Santana, Roberto and Mendiburu, Alexander and Lozano, José A},\n journal = {CoRR}\n}
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\n \n\n \n \n \n \n \n Comparison of Classification Methods for EEG-based Emotion Recognition.\n \n \n \n\n\n \n Zheng, W., L.; Santana, R.; and Lu, B., L.\n\n\n \n\n\n\n In Proceedings of the 2015 World Congress on Medical Physics and Biomedical Engineering, pages 1184-1187, 2015. Springer\n \n\n\n\n
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@inproceedings{\n title = {Comparison of Classification Methods for EEG-based Emotion Recognition},\n type = {inproceedings},\n year = {2015},\n keywords = {isg_ehu},\n pages = {1184-1187},\n publisher = {Springer},\n id = {e9dfc7e9-3d84-3c0f-b751-0541b5d5f4fc},\n created = {2021-11-12T08:31:20.742Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:20.742Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Zheng, W L and Santana, R and Lu, B L},\n booktitle = {Proceedings of the 2015 World Congress on Medical Physics and Biomedical Engineering}\n}
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\n \n\n \n \n \n \n \n Path Planning for Single Unmanned Aerial Vehicle by Separately Evolving Waypoints.\n \n \n \n\n\n \n Yang, P.; Tang, K.; Lozano, J., A.; and Cao, X.\n\n\n \n\n\n\n IEEE Transactions on Robotics, 31(5): 1130-1146. 2015.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Path Planning for Single Unmanned Aerial Vehicle by Separately Evolving Waypoints},\n type = {article},\n year = {2015},\n keywords = {isg_ehu,isg_jcr},\n pages = {1130-1146},\n volume = {31},\n id = {24c556fd-f771-30f8-b9fa-165d412cabe9},\n created = {2021-11-12T08:31:21.037Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:21.037Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Yang, P and Tang, K and Lozano, J A and Cao, X},\n journal = {IEEE Transactions on Robotics},\n number = {5}\n}
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\n \n\n \n \n \n \n \n Multi-objectivising the quadratic assignment problem by means of an elementary landscape decomposition.\n \n \n \n\n\n \n Ceberio, J.; Calvo, B.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n Volume 9422 2015.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@book{\n title = {Multi-objectivising the quadratic assignment problem by means of an elementary landscape decomposition},\n type = {book},\n year = {2015},\n keywords = {Elementary landscape decomposition,Interchange neighbourhood,Multi-objectivisation,Quadratic assignment problem},\n pages = {289-300},\n volume = {9422},\n id = {db879947-38b2-3716-b6ec-8e55625074ca},\n created = {2021-11-12T08:31:21.598Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:21.598Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {book},\n private_publication = {false},\n abstract = {© Springer International Publishing Switzerland 2015. In the last decade, many works in combinatorial optimisation have shown that, due to the advances in multi-objective optimisation, the algorithms in this field could be used for solving single-objective problems. In this sense, a number of papers have proposed multi-objectivising single-objective problems in order to apply multi-objectivisation schemes in their optimisation. In this paper, we follow this idea by presenting a method to multi-objectivise single-objective problems based on an elementary landscape decomposition of their objective function. In order to illustrate this procedure, we consider the elementary landscape decomposition of the Quadratic Assignment Problem under the interchange neighbourhood. In particular, we propose reformulating the QAP as a multi-objective problem, where each elementary landscape in the decomposition is an independent function to be optimised. In order to validate this multi-objectivisation scheme, we implement a version of NSGA-II for solving the multi-objective QAP, and compare its performance with that of a GA on the single-objective QAP. Conducted experiments show that the multi-objective approach is better than the single-objective approach for some types of instances.},\n bibtype = {book},\n author = {Ceberio, J and Calvo, B and Mendiburu, A and Lozano, J A}\n}
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\n © Springer International Publishing Switzerland 2015. In the last decade, many works in combinatorial optimisation have shown that, due to the advances in multi-objective optimisation, the algorithms in this field could be used for solving single-objective problems. In this sense, a number of papers have proposed multi-objectivising single-objective problems in order to apply multi-objectivisation schemes in their optimisation. In this paper, we follow this idea by presenting a method to multi-objectivise single-objective problems based on an elementary landscape decomposition of their objective function. In order to illustrate this procedure, we consider the elementary landscape decomposition of the Quadratic Assignment Problem under the interchange neighbourhood. In particular, we propose reformulating the QAP as a multi-objective problem, where each elementary landscape in the decomposition is an independent function to be optimised. In order to validate this multi-objectivisation scheme, we implement a version of NSGA-II for solving the multi-objective QAP, and compare its performance with that of a GA on the single-objective QAP. Conducted experiments show that the multi-objective approach is better than the single-objective approach for some types of instances.\n
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\n \n\n \n \n \n \n \n On the optimal usage of labelled examples in semi-supervised multi-class classification problems.\n \n \n \n\n\n \n Ortigosa-Hernández, J.; Inza, I.; and Lozano, J., A.\n\n\n \n\n\n\n Technical Report CCIA Department, University of the Basque Country UPV/EHU, San Sebastián, Spain, 2015.\n \n\n\n\n
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@techreport{\n title = {On the optimal usage of labelled examples in semi-supervised multi-class classification problems},\n type = {techreport},\n year = {2015},\n keywords = {isg_ehu,isg_jcr},\n issue = {EHU-KZAA-TR-2015-01},\n institution = {CCIA Department, University of the Basque Country UPV/EHU, San Sebastián, Spain},\n id = {816fa632-d805-3cf6-8f19-d9d47444548e},\n created = {2021-11-12T08:31:21.867Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:21.867Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {techreport},\n private_publication = {false},\n bibtype = {techreport},\n author = {Ortigosa-Hernández, J and Inza, I and Lozano, J A}\n}
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\n \n\n \n \n \n \n \n A review of travel time estimation and forecasting for Advanced Traveller Information Systems.\n \n \n \n\n\n \n Mori, U.; Mendiburu, A.; Álvarez, M.; and Lozano, J., A.\n\n\n \n\n\n\n Transportmetrica A: Transport Science, 11(2): 119-157. 2015.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {A review of travel time estimation and forecasting for Advanced Traveller Information Systems},\n type = {article},\n year = {2015},\n keywords = {isg_ehu,isg_jcr},\n pages = {119-157},\n volume = {11},\n id = {0d55cc01-b759-3a2d-8fd5-6c96eb0dbb42},\n created = {2021-11-12T08:31:28.361Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:28.361Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Mori, Usue and Mendiburu, Alexander and Álvarez, Maite and Lozano, Jose A},\n journal = {Transportmetrica A: Transport Science},\n number = {2}\n}
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\n \n\n \n \n \n \n \n A preliminary analysis on the effect of time series clustering on short term travel time prediction models.\n \n \n \n\n\n \n Mori, U.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n In International Work Conference on Time Series Analysis (ITISE2015), of ITISE 2015, 2015. \n \n\n\n\n
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@inproceedings{\n title = {A preliminary analysis on the effect of time series clustering on short term travel time prediction models.},\n type = {inproceedings},\n year = {2015},\n keywords = {isg_ehu},\n series = {ITISE 2015},\n id = {8f60a9ae-f8cf-3234-86a2-396b2980e89e},\n created = {2021-11-12T08:31:29.803Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:29.803Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Mori, U and Mendiburu, A and Lozano, J A},\n booktitle = {International Work Conference on Time Series Analysis (ITISE2015)}\n}
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\n \n\n \n \n \n \n \n Mixtures of Generalized Mallows models for solving the Quadratic Assignment Problem.\n \n \n \n\n\n \n Ceberio, J.; Santana, R.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n In 2015 IEEE Congress on Evolutionary Computation (CEC-2015), pages 2050-2057, 5 2015. \n \n\n\n\n
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@inproceedings{\n title = {Mixtures of Generalized Mallows models for solving the Quadratic Assignment Problem},\n type = {inproceedings},\n year = {2015},\n keywords = {isg_ehu},\n pages = {2050-2057},\n month = {5},\n id = {6576a0f2-bc7e-3e90-8a62-7e64380ce3cd},\n created = {2021-11-12T08:31:36.463Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:36.463Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Ceberio, Josu and Santana, Roberto and Mendiburu, Alexander and Lozano, Jose A},\n booktitle = {2015 IEEE Congress on Evolutionary Computation (CEC-2015)}\n}
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\n \n\n \n \n \n \n \n FrogCOL and FrogMIS: new decentralized algorithms for finding large independent sets in graphs.\n \n \n \n\n\n \n Blum, C.; Calvo, B.; and Blesa, M., J.\n\n\n \n\n\n\n Swarm Intelligence, 9(2-3): 205-227. 2015.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {FrogCOL and FrogMIS: new decentralized algorithms for finding large independent sets in graphs},\n type = {article},\n year = {2015},\n keywords = {isg_ehu,isg_jcr},\n pages = {205-227},\n volume = {9},\n publisher = {Springer US},\n id = {1a5bca3f-01c5-33a7-babb-2f7038bce9b9},\n created = {2021-11-12T08:31:37.033Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:37.033Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Blum, Christian and Calvo, Borja and Blesa, Maria J},\n journal = {Swarm Intelligence},\n number = {2-3}\n}
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\n \n\n \n \n \n \n \n Instances of Combinatorial Optimization Problems: Complexity and Generation.\n \n \n \n\n\n \n Hernando, L.\n\n\n \n\n\n\n Ph.D. Thesis, 2015.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@phdthesis{\n title = {Instances of Combinatorial Optimization Problems: Complexity and Generation},\n type = {phdthesis},\n year = {2015},\n institution = {University of the Basque Country},\n id = {26b92aae-ff8a-310e-ad4c-4d5c8132f98b},\n created = {2021-11-12T08:31:37.883Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:37.883Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {phdthesis},\n private_publication = {false},\n bibtype = {phdthesis},\n author = {Hernando, Leticia}\n}
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\n \n\n \n \n \n \n \n Capturing Relationships in Multi-Objective Optimization.\n \n \n \n\n\n \n Fritsche, G.; Strickler, A.; Pozo, A.; and Santana, R.\n\n\n \n\n\n\n In Proceedings of the Brazilian Conference on Intelligent Systems (BRACIS 2015), 2015. \n \n\n\n\n
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@inproceedings{\n title = {Capturing Relationships in Multi-Objective Optimization},\n type = {inproceedings},\n year = {2015},\n keywords = {isg_ehu},\n id = {1b4357bb-62d0-330e-8701-c9ffcea5bbc2},\n created = {2021-11-12T08:31:44.634Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:44.634Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Fritsche, G and Strickler, A and Pozo, A and Santana, R},\n booktitle = {Proceedings of the Brazilian Conference on Intelligent Systems (BRACIS 2015)}\n}
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\n \n\n \n \n \n \n \n A Boltzmann-Based Estimation of Distribution Algorithm for a General Resource Scheduling Model.\n \n \n \n\n\n \n Liang, X.; Chen, H.; and Lozano, J., A.\n\n\n \n\n\n\n IEEE Transactions on Evolutionary Computation, 19(6): 793-806. 2015.\n \n\n\n\n
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@article{\n title = {A Boltzmann-Based Estimation of Distribution Algorithm for a General Resource Scheduling Model},\n type = {article},\n year = {2015},\n keywords = {isg_ehu,isg_jcr},\n pages = {793-806},\n volume = {19},\n id = {7b148657-30b5-30d6-9b22-a4a96e04e421},\n created = {2021-11-12T08:31:48.889Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:48.889Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Liang, X and Chen, H and Lozano, J A},\n journal = {IEEE Transactions on Evolutionary Computation},\n number = {6}\n}
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\n \n\n \n \n \n \n \n Multi-objective NM-landscapes.\n \n \n \n\n\n \n Santana, R.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n In Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference, pages 1477-1478, 2015. \n \n\n\n\n
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@inproceedings{\n title = {Multi-objective NM-landscapes},\n type = {inproceedings},\n year = {2015},\n keywords = {isg_ehu},\n pages = {1477-1478},\n id = {d23774f1-b8f3-3cf9-9cc5-71182ad5d036},\n created = {2021-11-12T08:31:53.032Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:53.032Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Santana, R and Mendiburu, A and Lozano, J A},\n booktitle = {Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference}\n}
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\n \n\n \n \n \n \n \n A Survey of Performance Modeling and Simulation Techniques for Accelerator-Based Computing.\n \n \n \n\n\n \n Lopez-Novoa, U.; Mendiburu, A.; and Miguel-Alonso, J.\n\n\n \n\n\n\n IEEE Trans. Parallel Distrib. Syst., 26(1): 272-281. 2015.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {A Survey of Performance Modeling and Simulation Techniques for Accelerator-Based Computing},\n type = {article},\n year = {2015},\n keywords = {isg_ehu,isg_jcr},\n pages = {272-281},\n volume = {26},\n id = {932f50e4-c553-3cdb-bfeb-e3d0f93ff9be},\n created = {2021-11-12T08:31:53.316Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:53.316Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Lopez-Novoa, Unai and Mendiburu, Alexander and Miguel-Alonso, José},\n journal = {IEEE Trans. Parallel Distrib. Syst.},\n number = {1}\n}
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\n \n\n \n \n \n \n \n A novel weakly supervised problem: Learning from positive-unlabeled proportions.\n \n \n \n\n\n \n Hernández-González, J.; Inza, I.; and Lozano, J., A.\n\n\n \n\n\n\n In Proceedings of the 16th Conference of the Spanish Association for Artificial Intelligence, volume LNAI 9422, pages 3-13, 2015. \n \n\n\n\n
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@inproceedings{\n title = {A novel weakly supervised problem: Learning from positive-unlabeled proportions},\n type = {inproceedings},\n year = {2015},\n keywords = {isg_ehu},\n pages = {3-13},\n volume = {LNAI 9422},\n id = {6505c52a-46ce-307c-9dd6-62fb144e8af7},\n created = {2021-11-12T08:31:53.885Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:53.885Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Hernández-González, Jerónimo and Inza, Iñaki and Lozano, Jose A},\n booktitle = {Proceedings of the 16th Conference of the Spanish Association for Artificial Intelligence}\n}
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\n \n\n \n \n \n \n \n Kernel hautapen dinamikoa Optimizazio Bayesiarrean.\n \n \n \n\n\n \n Roman, I.; Santana, R.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n In Alegria, I.; and Omaetxebarria, M., J., editor(s), I. Ikergazte: Nazioarteko ikerketa euskaraz. Kongresuko artikulu-bilduma, pages 842, 2015. UEU\n \n\n\n\n
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@inproceedings{\n title = {Kernel hautapen dinamikoa Optimizazio Bayesiarrean},\n type = {inproceedings},\n year = {2015},\n keywords = {isg_ehu},\n pages = {842},\n publisher = {UEU},\n id = {81d18813-6f33-36b3-a5c0-2e85e4e8297e},\n created = {2021-11-12T08:31:54.454Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:54.454Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Roman, Ibai and Santana, Roberto and Mendiburu, Alexander and Lozano, Jose A},\n editor = {Alegria, Iñaki and Omaetxebarria, Miren J},\n booktitle = {I. Ikergazte: Nazioarteko ikerketa euskaraz. Kongresuko artikulu-bilduma}\n}
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\n \n\n \n \n \n \n \n Multi-objective environmental model evaluation by means of multidimensional kernel density estimators: Efficient and multi-core implementations.\n \n \n \n\n\n \n Lopez-Novoa, U.; Sáenz, J.; Mendiburu, A.; Miguel-Alonso, J.; Errasti, I.; Esnaola, G.; Ezcurra, A.; and Ibarra-Berastegi, G.\n\n\n \n\n\n\n Environmental Modelling and Software, 63: 123-136. 2015.\n \n\n\n\n
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\n 
@article{\n title = {Multi-objective environmental model evaluation by means of multidimensional kernel density estimators: Efficient and multi-core implementations},\n type = {article},\n year = {2015},\n keywords = {isg_ehu,isg_jcr},\n pages = {123-136},\n volume = {63},\n id = {656b564b-267a-317e-ab34-0a7de41008f8},\n created = {2021-11-12T08:31:54.743Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:54.743Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Lopez-Novoa, Unai and Sáenz, Jon and Mendiburu, Alexander and Miguel-Alonso, José and Errasti, Iñigo and Esnaola, Ganix and Ezcurra, Agust' and Ibarra-Berastegi, Gabriel},\n journal = {Environmental Modelling and Software}\n}
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\n \n\n \n \n \n \n \n An efficient implementation of kernel density estimation for multi-core and many-core architectures.\n \n \n \n\n\n \n Lopez-Novoa, U.; Sáenz, J.; Mendiburu, A.; and Miguel-Alonso, J.\n\n\n \n\n\n\n International Journal of High Performance Computing Applications, 29(3): 331-347. 2015.\n \n\n\n\n
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@article{\n title = {An efficient implementation of kernel density estimation for multi-core and many-core architectures},\n type = {article},\n year = {2015},\n keywords = {isg_ehu,isg_jcr},\n pages = {331-347},\n volume = {29},\n id = {65df8c8a-e688-3f9d-b63f-e56b2ca13142},\n created = {2021-11-12T08:31:59.145Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:59.145Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Lopez-Novoa, Unai and Sáenz, Jon and Mendiburu, Alexander and Miguel-Alonso, José},\n journal = {International Journal of High Performance Computing Applications},\n number = {3}\n}
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\n \n\n \n \n \n \n \n Proceedings of the 10th International Conference Simulated Evolution and Learning (SEAL-2014).\n \n \n \n\n\n \n Santana, R.; Mendiburu, A.; Lozano, J., A.; McDonald, R., B.; and Katzgraber, H., G.\n\n\n \n\n\n\n Springer, 2014.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@inbook{\n type = {inbook},\n year = {2014},\n keywords = {isg_ehu},\n publisher = {Springer},\n id = {f1a93fc7-f1e5-310c-90f8-69fdeb45bb7c},\n created = {2021-11-12T08:29:59.870Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:29:59.870Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n source_type = {incollection},\n private_publication = {false},\n bibtype = {inbook},\n author = {Santana, Roberto and Mendiburu, Alexander and Lozano, Jose A and McDonald, R B and Katzgraber, H G},\n chapter = {Proceedings of the 10th International Conference Simulated Evolution and Learning (SEAL-2014)}\n}
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\n \n\n \n \n \n \n \n A fast implementation of the first fit contiguous partitioning strategy for cubic topologies.\n \n \n \n\n\n \n Pascual, J., A.; Miguel-Alonso, J.; and Lozano, J., A.\n\n\n \n\n\n\n Concurrency and Computation: Practice and Experience, 26(17): 2792-2810. 2014.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {A fast implementation of the first fit contiguous partitioning strategy for cubic topologies},\n type = {article},\n year = {2014},\n keywords = {isg_ehu,isg_jcr},\n pages = {2792-2810},\n volume = {26},\n id = {f86e073f-61ae-3a8b-8baf-68b0e3654524},\n created = {2021-11-12T08:30:11.480Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:11.480Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Pascual, Jose A and Miguel-Alonso, José and Lozano, Jose A},\n journal = {Concurrency and Computation: Practice and Experience},\n number = {17}\n}
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\n \n\n \n \n \n \n \n Statistical hypothesis testing in positive unlabelled data.\n \n \n \n\n\n \n Sechidis, K.; Calvo, B.; and Brown, G.\n\n\n \n\n\n\n Volume 8726 LNAI 2014.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@book{\n title = {Statistical hypothesis testing in positive unlabelled data},\n type = {book},\n year = {2014},\n pages = {66-81},\n volume = {8726 LNAI},\n issue = {PART 3},\n id = {6f06d9c1-29b5-39f0-a0fe-53ded68ac897},\n created = {2021-11-12T08:30:19.550Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:19.550Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {book},\n private_publication = {false},\n abstract = {We propose a set of novel methodologies which enable valid statistical hypothesis testing when we have only positive and unlabelled (PU) examples. This type of problem, a special case of semi-supervised data, is common in text mining, bioinformatics, and computer vision. Focusing on a generalised likelihood ratio test, we have 3 key contributions: (1) a proof that assuming all unlabelled examples are negative cases is sufficient for independence testing, but not for power analysis activities; (2) a new methodology that compensates this and enables power analysis, allowing sample size determination for observing an effect with a desired power; and finally, (3) a new capability, supervision determination, which can determine a-priori the number of labelled examples the user must collect before being able to observe a desired statistical effect. Beyond general hypothesis testing, we suggest the tools will additionally be useful for information theoretic feature selection, and Bayesian Network structure learning. © 2014 Springer-Verlag.},\n bibtype = {book},\n author = {Sechidis, K and Calvo, B and Brown, G}\n}
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\n We propose a set of novel methodologies which enable valid statistical hypothesis testing when we have only positive and unlabelled (PU) examples. This type of problem, a special case of semi-supervised data, is common in text mining, bioinformatics, and computer vision. Focusing on a generalised likelihood ratio test, we have 3 key contributions: (1) a proof that assuming all unlabelled examples are negative cases is sufficient for independence testing, but not for power analysis activities; (2) a new methodology that compensates this and enables power analysis, allowing sample size determination for observing an effect with a desired power; and finally, (3) a new capability, supervision determination, which can determine a-priori the number of labelled examples the user must collect before being able to observe a desired statistical effect. Beyond general hypothesis testing, we suggest the tools will additionally be useful for information theoretic feature selection, and Bayesian Network structure learning. © 2014 Springer-Verlag.\n
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\n \n\n \n \n \n \n \n Global DNA methylation: Uncommon event in Oral Lichenoid Disease.\n \n \n \n\n\n \n Bediaga, N., G.; Marichalar-Medina, X.; Aguirre-Urizar, J., M.; Calvo, B.; Echebarria-Goikouria, M., A.; de Pancorbo, M., M.; and Acha-Sagredo, A.\n\n\n \n\n\n\n Oral Diseases, 8(20): 821-826. 2014.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Global DNA methylation: Uncommon event in Oral Lichenoid Disease},\n type = {article},\n year = {2014},\n keywords = {isg_ehu,isg_jcr},\n pages = {821-826},\n volume = {8},\n id = {122303e9-1805-32a6-81c3-87035e03af7e},\n created = {2021-11-12T08:30:25.399Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:25.399Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Bediaga, Naiara G and Marichalar-Medina, Xabier and Aguirre-Urizar, Jose M and Calvo, Borja and Echebarria-Goikouria, Maria A and de Pancorbo, Marian M and Acha-Sagredo, Amelia},\n journal = {Oral Diseases},\n number = {20}\n}
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\n \n\n \n \n \n \n \n A method for wind speed forecasting in airports based on non-parametric regression.\n \n \n \n\n\n \n Rozas-Larraondo, P.; Inza, I.; and Lozano, J., A.\n\n\n \n\n\n\n Weather and Forecasting, NA(http://dx.doi.org/10.1175/WAF-D-14-00006.1). 2014.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {A method for wind speed forecasting in airports based on non-parametric regression},\n type = {article},\n year = {2014},\n keywords = {isg_ehu,isg_jcr},\n volume = {NA},\n id = {1bdd22b6-eca1-3328-9383-4faf670ece95},\n created = {2021-11-12T08:30:33.455Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:33.455Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Rozas-Larraondo, Pablo and Inza, Iñaki and Lozano, Jose A},\n journal = {Weather and Forecasting},\n number = {http://dx.doi.org/10.1175/WAF-D-14-00006.1}\n}
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\n \n\n \n \n \n \n \n Multiobjective Estimation of Distribution Algorithm Based on Joint Modeling of Objectives and Variables.\n \n \n \n\n\n \n Karshenas, H.; Santana, R.; Bielza, C.; and Larrañaga, P.\n\n\n \n\n\n\n IEEE Transactions on Evolutionary Computation, 18(4): 519-542. 2014.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Multiobjective Estimation of Distribution Algorithm Based on Joint Modeling of Objectives and Variables},\n type = {article},\n year = {2014},\n keywords = {isg_ehu,isg_jcr},\n pages = {519-542},\n volume = {18},\n id = {011cae8a-a7e3-39ac-b1e6-5d0302e22df2},\n created = {2021-11-12T08:30:52.065Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:52.065Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Karshenas, H and Santana, R and Bielza, C and Larrañaga, P},\n journal = {IEEE Transactions on Evolutionary Computation},\n number = {4}\n}
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\n \n\n \n \n \n \n \n Distributed Estimation of Distribution Algorithms for continuous optimization: How does the exchanged information influence their behavior?.\n \n \n \n\n\n \n Muelas, S.; Mendiburu, A.; LaTorre, A.; and Peña, J., M.\n\n\n \n\n\n\n Inf. Sci., 268: 231-254. 2014.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Distributed Estimation of Distribution Algorithms for continuous optimization: How does the exchanged information influence their behavior?},\n type = {article},\n year = {2014},\n keywords = {isg_ehu,isg_jcr},\n pages = {231-254},\n volume = {268},\n id = {b107d5d1-143c-32cf-bc3b-ee12c759c720},\n created = {2021-11-12T08:30:59.642Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:59.642Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Muelas, Santiago and Mendiburu, Alexander and LaTorre, Antonio and Peña, José M},\n journal = {Inf. Sci.}\n}
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\n \n\n \n \n \n \n \n Dynamic Kernel Selection Criteria for Bayesian Optimization.\n \n \n \n\n\n \n Roman, I.; Santana, R.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n In BayesOpt 2014: NIPS Workshop on Bayesian Optimization, 2014. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Dynamic Kernel Selection Criteria for Bayesian Optimization},\n type = {inproceedings},\n year = {2014},\n keywords = {isg_ehu},\n id = {f48447f8-4d7e-39bc-8ea5-9c17e6cb2d53},\n created = {2021-11-12T08:31:07.905Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:07.905Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Roman, Ibai and Santana, Roberto and Mendiburu, Alexander and Lozano, Jose A},\n booktitle = {BayesOpt 2014: NIPS Workshop on Bayesian Optimization}\n}
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\n \n\n \n \n \n \n \n A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments.\n \n \n \n\n\n \n Lorido-Botrán, T.; Miguel-Alonso, J.; and Lozano, J., A.\n\n\n \n\n\n\n Journal of Grid Computing, NA: 1-34. 2014.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments},\n type = {article},\n year = {2014},\n keywords = {isg_ehu,isg_jcr},\n pages = {1-34},\n volume = {NA},\n id = {066cd61f-2cd4-319d-87af-2a117ac7e1ba},\n created = {2021-11-12T08:31:08.696Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:08.696Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Lorido-Botrán, Tania and Miguel-Alonso, José and Lozano, Jose A},\n journal = {Journal of Grid Computing}\n}
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\n \n\n \n \n \n \n \n Sampling and learning the Mallows and Generalized Mallows models under the Cayley distance.\n \n \n \n\n\n \n Irurozki, E.; Calvo, B.; and Lozano, J., A.\n\n\n \n\n\n\n Methodology and Computing in Applied Probability (submitted). 2014.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Sampling and learning the Mallows and Generalized Mallows models under the Cayley distance},\n type = {article},\n year = {2014},\n keywords = {isg_,isg_ehu,isg_jcr},\n institution = {University of the Basque Country},\n id = {dd2c6fa5-c6e5-3877-b1b7-8a1611fdd098},\n created = {2021-11-12T08:31:14.635Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:14.635Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Irurozki, Ekhine and Calvo, Borja and Lozano, Jose A},\n journal = {Methodology and Computing in Applied Probability (submitted)}\n}
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\n \n\n \n \n \n \n \n Extending Distance-based Ranking Models in Estimation of Distribution Algorithms.\n \n \n \n\n\n \n Ceberio, J.; Irurozki, E.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n In Proceedings of the 2014 IEEE Congress on Evolutionary Computation, pages 2459-2466, 2014. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Extending Distance-based Ranking Models in Estimation of Distribution Algorithms},\n type = {inproceedings},\n year = {2014},\n keywords = {isg_ehu},\n pages = {2459-2466},\n id = {b9e4be73-ec93-37aa-9682-5fbc701b45ec},\n created = {2021-11-12T08:31:21.335Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:21.335Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Ceberio, Josu and Irurozki, Ekhine and Mendiburu, Alexander and Lozano, Jose A},\n booktitle = {Proceedings of the 2014 IEEE Congress on Evolutionary Computation}\n}
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\n \n\n \n \n \n \n \n Application-aware metrics for partition selection in cube-shaped topologies.\n \n \n \n\n\n \n Pascual, J., A.; Miguel-Alonso, J.; and Lozano, J., A.\n\n\n \n\n\n\n Parallel Computing, 40(05/06/2016): 129-139. 2014.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Application-aware metrics for partition selection in cube-shaped topologies},\n type = {article},\n year = {2014},\n keywords = {isg_ehu,isg_jcr},\n pages = {129-139},\n volume = {40},\n id = {b4568491-d8c0-390f-963d-dbe5f61a001d},\n created = {2021-11-12T08:31:24.366Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:24.366Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Pascual, Jose A and Miguel-Alonso, José and Lozano, Jose A},\n journal = {Parallel Computing},\n number = {05/06/2016}\n}
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\n \n\n \n \n \n \n \n Mallows Model under the Ulam Distance: a Feasible Combinatorial Approach.\n \n \n \n\n\n \n Irurozki, E.; Calvo, B.; and Lozano, J., A.\n\n\n \n\n\n\n In Neural Information Processing System (NIPS), Analysis on Rank Data workshop, 2014. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Mallows Model under the Ulam Distance: a Feasible Combinatorial Approach},\n type = {inproceedings},\n year = {2014},\n keywords = {isg_,isg_ehu},\n id = {634d65ec-d464-39b4-97c8-d533c37a46ed},\n created = {2021-11-12T08:31:29.518Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:29.518Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Irurozki, Ekhine and Calvo, Borja and Lozano, Jose A},\n booktitle = {Neural Information Processing System (NIPS), Analysis on Rank Data workshop}\n}
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\n \n\n \n \n \n \n \n Solving Permutation Problems with Estimation of Distribution Algorithms and Extensions Thereof.\n \n \n \n\n\n \n Ceberio, J.\n\n\n \n\n\n\n Ph.D. Thesis, 2014.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@phdthesis{\n title = {Solving Permutation Problems with Estimation of Distribution Algorithms and Extensions Thereof},\n type = {phdthesis},\n year = {2014},\n keywords = {isg_ehu,isg_jcr},\n institution = {Faculty of Computer Science, University of the Basque Country},\n id = {f964c9bd-fef6-3287-b864-ccd70265ebb5},\n created = {2021-11-12T08:31:31.313Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:31.313Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {phdthesis},\n private_publication = {false},\n bibtype = {phdthesis},\n author = {Ceberio, Josu}\n}
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\n \n\n \n \n \n \n \n Sampling and learning distance-based probability models for permutation spaces.\n \n \n \n\n\n \n Irurozki, E.\n\n\n \n\n\n\n Ph.D. Thesis, 2014.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@phdthesis{\n title = {Sampling and learning distance-based probability models for permutation spaces},\n type = {phdthesis},\n year = {2014},\n institution = {University of the Basque Country},\n id = {29c47a11-c8b5-3867-94ce-bfc9dd2fccbf},\n created = {2021-11-12T08:31:39.563Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:39.563Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {phdthesis},\n private_publication = {false},\n bibtype = {phdthesis},\n author = {Irurozki, Ekhine}\n}
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\n \n\n \n \n \n \n \n Can frogs find large independent sets in a decentralized way? Yes they can!.\n \n \n \n\n\n \n Blum, C.; Blesa, M., J.; and Calvo, B.\n\n\n \n\n\n\n Volume 8667 2014.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@book{\n title = {Can frogs find large independent sets in a decentralized way? Yes they can!},\n type = {book},\n year = {2014},\n pages = {74-85},\n volume = {8667},\n id = {ae3f750d-53f4-3ed8-87f1-18f15710b443},\n created = {2021-11-12T08:31:39.831Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:39.831Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {book},\n private_publication = {false},\n abstract = {© Springer International Publishing Switzerland 2014. The problem of identifying a maximal independent (node) set in a given graph is a fundamental problem in distributed computing. It has numerous applications, for example, in wireless networks in the context of facility location and backbone formation. In this paper we study the ability of a bio-inspired, distributed algorithm, initially proposed for graph coloring, to generate large independent sets. The inspiration of the considered algorithm stems from the self-synchronization capability of Japanese tree frogs. The experimental results confirm, indeed, that the algorithm has a strong tendency towards the generation of colorings in which the set of nodes assigned to the most-used color is rather large. Experimental results are compared to the ones of recent algorithms from the literature. Concerning solution quality, the results show that the frog-inspired algorithm has advantages especially for the application to rather sparse graphs. Concerning the computation round count, the algorithm has the advantage of converging within a reasonable number of iterations, regardless of the size and density of the considered graph.},\n bibtype = {book},\n author = {Blum, C and Blesa, M J and Calvo, B}\n}
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\n © Springer International Publishing Switzerland 2014. The problem of identifying a maximal independent (node) set in a given graph is a fundamental problem in distributed computing. It has numerous applications, for example, in wireless networks in the context of facility location and backbone formation. In this paper we study the ability of a bio-inspired, distributed algorithm, initially proposed for graph coloring, to generate large independent sets. The inspiration of the considered algorithm stems from the self-synchronization capability of Japanese tree frogs. The experimental results confirm, indeed, that the algorithm has a strong tendency towards the generation of colorings in which the set of nodes assigned to the most-used color is rather large. Experimental results are compared to the ones of recent algorithms from the literature. Concerning solution quality, the results show that the frog-inspired algorithm has advantages especially for the application to rather sparse graphs. Concerning the computation round count, the algorithm has the advantage of converging within a reasonable number of iterations, regardless of the size and density of the considered graph.\n
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\n \n\n \n \n \n \n \n The Linear Ordering Problem Revisited.\n \n \n \n\n\n \n Ceberio, J.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n European Journal of Operational Research, 241(3): 686-696. 2014.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {The Linear Ordering Problem Revisited},\n type = {article},\n year = {2014},\n keywords = {isg_ehu,isg_jcr},\n pages = {686-696},\n volume = {241},\n id = {e58fdb18-75d8-3a2a-8c8a-66c24b993274},\n created = {2021-11-12T08:31:48.609Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:48.609Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Ceberio, Josu and Mendiburu, Alexander and Lozano, Jose A},\n journal = {European Journal of Operational Research},\n number = {3}\n}
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\n \n\n \n \n \n \n \n A Distance-based Ranking Model Estimation of Distribution Algorithm for the Flowshop Scheduling Problem.\n \n \n \n\n\n \n Ceberio, J.; Irurozki, E.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n IEEE Transactions on Evolutionary Computation, 18(2): 286-300. 2014.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {A Distance-based Ranking Model Estimation of Distribution Algorithm for the Flowshop Scheduling Problem},\n type = {article},\n year = {2014},\n keywords = {isg_ehu,isg_jcr},\n pages = {286-300},\n volume = {18},\n id = {fa30de2b-0bcf-30d5-9020-d65767d64a6b},\n created = {2021-11-12T08:31:55.314Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:55.314Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Ceberio, Josu and Irurozki, Ekhine and Mendiburu, Alexander and Lozano, Jose A},\n journal = {IEEE Transactions on Evolutionary Computation},\n number = {2}\n}
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\n \n\n \n \n \n \n \n Assisting in search heuristics selection through multidimensional supervised classification: A case study on software testing.\n \n \n \n\n\n \n Sagarna, R.; Mendiburu, A.; Inza, I.; and Lozano, J., A.\n\n\n \n\n\n\n Inf. Sci., 258: 122-139. 2014.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Assisting in search heuristics selection through multidimensional supervised classification: A case study on software testing},\n type = {article},\n year = {2014},\n keywords = {isg_ehu,isg_jcr},\n pages = {122-139},\n volume = {258},\n id = {eed40e72-a817-3357-b112-060f6b12f339},\n created = {2021-11-12T08:31:56.945Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:56.945Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Sagarna, Ramón and Mendiburu, Alexander and Inza, Iñaki and Lozano, José A},\n journal = {Inf. Sci.}\n}
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\n  \n 2013\n \n \n (27)\n \n \n
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\n \n\n \n \n \n \n \n Intelligent Data Engineering and Automated Learning - IDEAL 2013: 14th International Conference, IDEAL 2013, Hefei, China, October 20-23, 2013. Proceedings.\n \n \n \n\n\n \n Ceberio, J.; Hernando, L.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n Volume LNCS 8206 . pages 479-486. Yin, H.; Tang, K.; Gao, Y.; Klawonn, F.; Lee, M.; Weise, T.; Li, B.; and Yao, X., editor(s). Springer Berlin Heidelberg, 2013.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@inbook{\n type = {inbook},\n year = {2013},\n keywords = {isg_ehu},\n pages = {479-486},\n volume = {LNCS 8206},\n publisher = {Springer Berlin Heidelberg},\n chapter = {Intelligent Data Engineering and Automated Learning - IDEAL 2013: 14th International Conference, IDEAL 2013, Hefei, China, October 20-23, 2013. Proceedings},\n id = {e853c960-80ba-3bc0-9ae3-bbc186ac6129},\n created = {2021-11-12T08:30:03.716Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:03.716Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {incollection},\n private_publication = {false},\n bibtype = {inbook},\n author = {Ceberio, Josu and Hernando, Leticia and Mendiburu, Alexander and Lozano, Jose A},\n editor = {Yin, Hujun and Tang, Ke and Gao, Yang and Klawonn, Frank and Lee, Minho and Weise, Thomas and Li, Bin and Yao, Xin}\n}
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\n \n\n \n \n \n \n \n Regularized Continuous Estimation of Distribution Algorithms.\n \n \n \n\n\n \n Karshenas, H.; Santana, R.; Bielza, C.; and Larrañaga, P.\n\n\n \n\n\n\n Applied Soft Computing, 13(5): 2412-2432. 2013.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Regularized Continuous Estimation of Distribution Algorithms},\n type = {article},\n year = {2013},\n keywords = {isg_ehu,isg_jcr},\n pages = {2412-2432},\n volume = {13},\n id = {29335ec8-3eb5-387a-9c2d-d19bbb7067d2},\n created = {2021-11-12T08:30:09.415Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:09.415Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Karshenas, Hossein and Santana, Roberto and Bielza, Concha and Larrañaga, Pedro},\n journal = {Applied Soft Computing},\n number = {5}\n}
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\n \n\n \n \n \n \n \n An evaluation of methods for estimating the number of local optima in combinatorial optimization problems.\n \n \n \n\n\n \n Hernando, L.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n Evolutionary Computation, 21(4): 625-658. 2013.\n \n\n\n\n
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@article{\n title = {An evaluation of methods for estimating the number of local optima in combinatorial optimization problems},\n type = {article},\n year = {2013},\n keywords = {isg_ehu,isg_jcr},\n pages = {625-658},\n volume = {21},\n id = {3b21c3a0-1dd8-3a72-b1c3-b512c38eb220},\n created = {2021-11-12T08:30:11.751Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:11.751Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Hernando, Leticia and Mendiburu, Alexander and Lozano, Jose A},\n journal = {Evolutionary Computation},\n number = {4}\n}
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\n \n\n \n \n \n \n \n Artificial Evolution.\n \n \n \n\n\n \n Blum, C.; Blesa, M., J.; and Calvo, B.\n\n\n \n\n\n\n pages 79-90. Springer International Publishing, 2013.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inbook{\n type = {inbook},\n year = {2013},\n keywords = {isg_ehu},\n pages = {79-90},\n publisher = {Springer International Publishing},\n chapter = {Artificial Evolution},\n id = {c493da82-6453-3317-81fe-2404beca71a9},\n created = {2021-11-12T08:30:15.407Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:15.407Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {incollection},\n private_publication = {false},\n bibtype = {inbook},\n author = {Blum, Christian and Blesa, Maria J and Calvo, Borja}\n}
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\n \n\n \n \n \n \n \n Learning Bayesian network classifiers from label proportions.\n \n \n \n\n\n \n Hernández-González, J.; Inza, I.; and Lozano, J., A.\n\n\n \n\n\n\n Pattern Recognition, 46(12): 3425-3440. 2013.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Learning Bayesian network classifiers from label proportions},\n type = {article},\n year = {2013},\n keywords = {isg_ehu,isg_jcr},\n pages = {3425-3440},\n volume = {46},\n id = {c15ee885-0c4b-3b1d-957f-496f71fba1d3},\n created = {2021-11-12T08:30:20.379Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:20.379Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Hernández-González, Jerónimo and Inza, Iñaki and Lozano, Jose A},\n journal = {Pattern Recognition},\n number = {12}\n}
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\n \n\n \n \n \n \n \n Message passing methods for estimation of distribution algorithms based on Markov networks.\n \n \n \n\n\n \n Santana, R.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n In Proceedings of the 4th Conference on Swarm, Evolutionary, and Memetic Computing (SEMCCO-2013), of Lectures Notes in Computer Science, pages 419-430, 2013. Springer\n \n\n\n\n
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\n 
@inproceedings{\n title = {Message passing methods for estimation of distribution algorithms based on Markov networks},\n type = {inproceedings},\n year = {2013},\n keywords = {isg_ehu},\n pages = {419-430},\n publisher = {Springer},\n series = {Lectures Notes in Computer Science},\n id = {3d5a8bd6-9d7d-3987-b4dd-b80ffce55a1b},\n created = {2021-11-12T08:30:26.779Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:26.779Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Santana, R and Mendiburu, A and Lozano, J A},\n booktitle = {Proceedings of the 4th Conference on Swarm, Evolutionary, and Memetic Computing (SEMCCO-2013)}\n}
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\n \n\n \n \n \n \n \n A General Framework for the Statistical Analysis of the Sources of Variance for Classification Error Estimators.\n \n \n \n\n\n \n Rodríguez, J., D.; Pérez, A.; and Lozano, J., A.\n\n\n \n\n\n\n Pattern Recognition, 46(3): 855-864. 2013.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {A General Framework for the Statistical Analysis of the Sources of Variance for Classification Error Estimators},\n type = {article},\n year = {2013},\n keywords = {isg_ehu,isg_jcr},\n pages = {855-864},\n volume = {46},\n id = {c1b82e3f-da29-3fd0-ad8f-42cee44a93a4},\n created = {2021-11-12T08:30:27.836Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:27.836Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Rodríguez, Juan D and Pérez, Aritz and Lozano, Jose A},\n journal = {Pattern Recognition},\n number = {3}\n}
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\n \n\n \n \n \n \n \n On the Taxonomy of Optimization Problems Under Estimation of Distribution Algorithms.\n \n \n \n\n\n \n Echegoyen, C.; Mendiburu, A.; Santana, R.; and Lozano, J., A.\n\n\n \n\n\n\n Evolutionary Computation, 21(3): 471-495. 2013.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {On the Taxonomy of Optimization Problems Under Estimation of Distribution Algorithms},\n type = {article},\n year = {2013},\n keywords = {isg_ehu,isg_jcr},\n pages = {471-495},\n volume = {21},\n id = {821f424e-8638-3ee6-9681-d3ec84878df3},\n created = {2021-11-12T08:30:36.442Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:36.442Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Echegoyen, Carlos and Mendiburu, Alexander and Santana, Roberto and Lozano, José A},\n journal = {Evolutionary Computation},\n number = {3}\n}
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\n \n\n \n \n \n \n \n Symmetry in evolutionary and estimation of distribution algorithms.\n \n \n \n\n\n \n Santana, R.; McKay, R., I.; and Lozano, J., A.\n\n\n \n\n\n\n In Proceedings of the 2013 Congress on Evolutionary Computation CEC-2013, 2013. IEEE Press\n \n\n\n\n
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\n 
@inproceedings{\n title = {Symmetry in evolutionary and estimation of distribution algorithms},\n type = {inproceedings},\n year = {2013},\n keywords = {isg_ehu},\n publisher = {IEEE Press},\n id = {76f4b26e-9541-3756-8b88-1e145afdce89},\n created = {2021-11-12T08:30:37.565Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:37.565Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Santana, R and McKay, R I and Lozano, J A},\n booktitle = {Proceedings of the 2013 Congress on Evolutionary Computation CEC-2013}\n}
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\n \n\n \n \n \n \n \n Time Series Database Characterization and Similarity Measure Selection.\n \n \n \n\n\n \n Mori, U.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n In Actas del VII Simposio de Teoría y Aplicaciones de Minería de Datos (TAMIDA 2013), of CEDI 2013, pages 1427-1436, 2013. \n \n\n\n\n
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@inproceedings{\n title = {Time Series Database Characterization and Similarity Measure Selection},\n type = {inproceedings},\n year = {2013},\n keywords = {isg_ehu},\n pages = {1427-1436},\n series = {CEDI 2013},\n id = {1586ebda-9a26-3e73-b0d5-ea44009f14aa},\n created = {2021-11-12T08:30:38.088Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:38.088Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Mori, U and Mendiburu, A and Lozano, J A},\n booktitle = {Actas del VII Simposio de Teoría y Aplicaciones de Minería de Datos (TAMIDA 2013)}\n}
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\n \n\n \n \n \n \n \n Performance Evaluation of Interconnection Networks Using Simulation: Tools and Case Studies.\n \n \n \n\n\n \n Ridruejo, J.\n\n\n \n\n\n\n Ph.D. Thesis, 2013.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@phdthesis{\n title = {Performance Evaluation of Interconnection Networks Using Simulation: Tools and Case Studies},\n type = {phdthesis},\n year = {2013},\n institution = {University of the Basque Country},\n id = {928ed4a1-490f-338e-9bc8-2e2a2fbdd860},\n created = {2021-11-12T08:30:43.985Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:43.985Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {phdthesis},\n private_publication = {false},\n bibtype = {phdthesis},\n author = {Ridruejo, Javier}\n}
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\n \n\n \n \n \n \n \n High throughput computing over peer-to-peer networks.\n \n \n \n\n\n \n Pérez-Miguel, C.; Miguel-Alonso, J.; and Mendiburu, A.\n\n\n \n\n\n\n Future Generation Comp. Syst., 29(1): 352-360. 2013.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {High throughput computing over peer-to-peer networks},\n type = {article},\n year = {2013},\n keywords = {isg_ehu,isg_jcr},\n pages = {352-360},\n volume = {29},\n id = {85bdc577-7dd5-3930-9b97-bba20e325f61},\n created = {2021-11-12T08:30:44.253Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:44.253Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Pérez-Miguel, Carlos and Miguel-Alonso, José and Mendiburu, Alexander},\n journal = {Future Generation Comp. Syst.},\n number = {1}\n}
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\n \n\n \n \n \n \n \n Mechanisms and techniques for scheduling in supercomputers.\n \n \n \n\n\n \n Pascual, J., A.\n\n\n \n\n\n\n Ph.D. Thesis, 2013.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@phdthesis{\n title = {Mechanisms and techniques for scheduling in supercomputers},\n type = {phdthesis},\n year = {2013},\n institution = {University of the Basque Country},\n id = {2ac7e9b9-6842-34bc-b8fb-09bd1a920627},\n created = {2021-11-12T08:30:52.937Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:52.937Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {phdthesis},\n private_publication = {false},\n bibtype = {phdthesis},\n author = {Pascual, Jose A}\n}
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\n \n\n \n \n \n \n \n A Review on Evolutionary Algorithms in Bayesian Network Learning and Inference Tasks.\n \n \n \n\n\n \n Larrañaga, P.; Karshenas, H.; Bielza, C.; and Santana, R.\n\n\n \n\n\n\n Information Sciences, 233: 109-125. 2013.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {A Review on Evolutionary Algorithms in Bayesian Network Learning and Inference Tasks},\n type = {article},\n year = {2013},\n keywords = {isg_ehu,isg_jcr},\n pages = {109-125},\n volume = {233},\n id = {705b48e3-ea49-3c7e-98cf-fe3c74479f8f},\n created = {2021-11-12T08:30:53.472Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:53.472Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Larrañaga, P and Karshenas, H and Bielza, C and Santana, R},\n journal = {Information Sciences}\n}
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\n \n\n \n \n \n \n \n SpiNNaker: Fault Tolerance in a Power- and Area- Constrained Large-Scale Neuromimetic Architecture.\n \n \n \n\n\n \n Navaridas, J.; Furber, S.; Luján, M.; and Miguel-Alonso, J.\n\n\n \n\n\n\n Parallel Computing, 11(39). 2013.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {SpiNNaker: Fault Tolerance in a Power- and Area- Constrained Large-Scale Neuromimetic Architecture},\n type = {article},\n year = {2013},\n keywords = {isg_ehu,isg_jcr},\n volume = {11},\n id = {4e2cda48-e2f1-3679-b847-42dfc6682a5e},\n created = {2021-11-12T08:30:55.312Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:55.312Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Navaridas, Javier and Furber, Steve and Luján, Mikel and Miguel-Alonso, José},\n journal = {Parallel Computing},\n number = {39}\n}
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\n \n\n \n \n \n \n \n Learning from crowds in multi-dimensional classification domains.\n \n \n \n\n\n \n Hernández-González, J.; Inza, I.; and Lozano, J., A.\n\n\n \n\n\n\n In Proceedings of the 15th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2013, Madrid, Spain, September 17-20, 2013., volume LNAI 8109, pages 352-362, 2013. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Learning from crowds in multi-dimensional classification domains},\n type = {inproceedings},\n year = {2013},\n keywords = {isg_ehu},\n pages = {352-362},\n volume = {LNAI 8109},\n id = {d1d38150-c7e5-3203-b978-dbe16317c32b},\n created = {2021-11-12T08:30:59.906Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:59.906Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Hernández-González, Jerónimo and Inza, Iñaki and Lozano, Jose A},\n booktitle = {Proceedings of the 15th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2013, Madrid, Spain, September 17-20, 2013.}\n}
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\n \n\n \n \n \n \n \n A new measure for gene expression biclustering based on non-parametric correlation.\n \n \n \n\n\n \n Flores, J., L.; Inza, I.; Larrañaga, P.; and Calvo, B.\n\n\n \n\n\n\n Computer Methods and Programs in Biomedicine, 3(112): 367-397. 2013.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {A new measure for gene expression biclustering based on non-parametric correlation},\n type = {article},\n year = {2013},\n keywords = {isg_ehu,isg_jcr},\n pages = {367-397},\n volume = {3},\n id = {29f8cc30-ad1f-33bf-a285-2248b1ff7f8c},\n created = {2021-11-12T08:31:00.829Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:00.829Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Flores, Jose L and Inza, Iñaki and Larrañaga, Pedro and Calvo, Borja},\n journal = {Computer Methods and Programs in Biomedicine},\n number = {112}\n}
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\n \n\n \n \n \n \n \n Significance tests or confidence intervals: which are preferable for the comparison of classifiers?.\n \n \n \n\n\n \n Berrar, D.; and Lozano, J., A.\n\n\n \n\n\n\n Journal of Experimental & Theoretical Artificial Intelligence, 25(2): 189-206. 2013.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Significance tests or confidence intervals: which are preferable for the comparison of classifiers?},\n type = {article},\n year = {2013},\n keywords = {isg_ehu,isg_jcr},\n pages = {189-206},\n volume = {25},\n publisher = {Taylor & Francis},\n id = {a71fa8a0-5fdb-3d51-8c50-d419c82957ba},\n created = {2021-11-12T08:31:23.525Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:23.525Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Berrar, Daniel and Lozano, Jose A},\n journal = {Journal of Experimental & Theoretical Artificial Intelligence},\n number = {2}\n}
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\n \n\n \n \n \n \n \n Generating customized landscapes in permutation-based combinatorial optimization problems.\n \n \n \n\n\n \n Hernando, L.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n In Learning and Intelligent OptimizatioN Conference (LION 7), volume 7997, of Lecture Notes in Computer Science, pages 299-303, 2013. Springer Berlin Heidelber\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Generating customized landscapes in permutation-based combinatorial optimization problems},\n type = {inproceedings},\n year = {2013},\n keywords = {isg_ehu},\n pages = {299-303},\n volume = {7997},\n publisher = {Springer Berlin Heidelber},\n series = {Lecture Notes in Computer Science},\n id = {6a282b2f-fe70-331c-90fd-a1bf7afd8b98},\n created = {2021-11-12T08:31:25.162Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:25.162Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Hernando, Leticia and Mendiburu, Alexander and Lozano, Jose A},\n booktitle = {Learning and Intelligent OptimizatioN Conference (LION 7)}\n}
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\n \n\n \n \n \n \n \n Generador de instancias de problemas de optimizacion combinatoria basados en permutaciones.\n \n \n \n\n\n \n Hernando, L.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n In IX Congreso Español sobre Metaheuristicas, Algoritmos Evolutivos y Bioinspirados (MAEB 2013), 2013. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Generador de instancias de problemas de optimizacion combinatoria basados en permutaciones},\n type = {inproceedings},\n year = {2013},\n keywords = {isg_ehu},\n id = {242bdbd0-96f6-3874-befd-257b56c8d0c8},\n created = {2021-11-12T08:31:31.612Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:31.612Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Hernando, Leticia and Mendiburu, Alexander and Lozano, Jose A},\n booktitle = {IX Congreso Español sobre Metaheuristicas, Algoritmos Evolutivos y Bioinspirados (MAEB 2013)}\n}
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\n \n\n \n \n \n \n \n Critical issues in model-based surrogate functions in estimation of distribution algorithms.\n \n \n \n\n\n \n Santana, R.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n In Proceedings of the 4th Conference on Swarm, Evolutionary, and Memetic Computing (SEMCCO-2013), of Lectures Notes in Computer Science, pages 1-13, 2013. Springer\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Critical issues in model-based surrogate functions in estimation of distribution algorithms},\n type = {inproceedings},\n year = {2013},\n keywords = {isg_ehu},\n pages = {1-13},\n publisher = {Springer},\n series = {Lectures Notes in Computer Science},\n id = {b541425c-1ab2-3499-aa4e-5032ae9ae921},\n created = {2021-11-12T08:31:41.486Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:41.486Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Santana, R and Mendiburu, A and Lozano, J A},\n booktitle = {Proceedings of the 4th Conference on Swarm, Evolutionary, and Memetic Computing (SEMCCO-2013)}\n}
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\n \n\n \n \n \n \n \n Supervised pre-processing approaches in multiple class variables classification for fish recruitment forecasting.\n \n \n \n\n\n \n Fernandes, J., A.; Lozano, J., A.; Inza, I.; Irigoien, X.; Pérez, A.; and Rodr\\'\\iguez, J., D.\n\n\n \n\n\n\n Environmental Modelling & Software, 40: 245-254. 2013.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Supervised pre-processing approaches in multiple class variables classification for fish recruitment forecasting},\n type = {article},\n year = {2013},\n keywords = {isg_ehu,isg_jcr},\n pages = {245-254},\n volume = {40},\n id = {f47da40c-00a2-371a-b47e-f314ce7023ce},\n created = {2021-11-12T08:31:42.600Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:42.600Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Fernandes, Jose A and Lozano, Jose A and Inza, Iñaki and Irigoien, Xabier and Pérez, Aritz and Rodr\\'\\iguez, Juan D},\n journal = {Environmental Modelling & Software}\n}
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\n \n\n \n \n \n \n \n Classification of neocortical interneurons using affinity propagation.\n \n \n \n\n\n \n Santana, R.; McGarry, L.; Bielza, C.; Larrañaga, P.; and Yuste, R.\n\n\n \n\n\n\n Frontiers in Neural Circuits. 2013.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Classification of neocortical interneurons using affinity propagation},\n type = {article},\n year = {2013},\n keywords = {isg_ehu,isg_jcr},\n id = {de7bc0ed-bf0f-31cd-b47a-a748e9d14db8},\n created = {2021-11-12T08:31:43.189Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:43.189Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Santana, R and McGarry, L and Bielza, C and Larrañaga, P and Yuste, R},\n journal = {Frontiers in Neural Circuits}\n}
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\n \n\n \n \n \n \n \n Advances in Error Estimation and Multi-Dimensional Supervised Classification.\n \n \n \n\n\n \n Diego Rodríguez, J.\n\n\n \n\n\n\n Ph.D. Thesis, 2013.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@phdthesis{\n title = {Advances in Error Estimation and Multi-Dimensional Supervised Classification},\n type = {phdthesis},\n year = {2013},\n institution = {University of the Basque Country},\n id = {1b511802-c16b-31fa-a449-180b80d98499},\n created = {2021-11-12T08:31:49.158Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:49.158Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {phdthesis},\n private_publication = {false},\n bibtype = {phdthesis},\n author = {Diego Rodríguez, Juan}\n}
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\n \n\n \n \n \n \n \n Network measures for information extraction in evolutionary algorithms.\n \n \n \n\n\n \n Santana, R.; Armañanzas, R.; Bielza, C.; and Larrañaga, P.\n\n\n \n\n\n\n International Journal of Computational Intelligence Systems, 6(6): 1163-1188. 2013.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Network measures for information extraction in evolutionary algorithms},\n type = {article},\n year = {2013},\n keywords = {isg_ehu,isg_jcr},\n pages = {1163-1188},\n volume = {6},\n id = {6ce60cf7-7168-3a26-8ce5-fe924253f239},\n created = {2021-11-12T08:31:50.019Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:50.019Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {Problem domain information extraction is a critical issue in many real-world optimization problems. Increasing the repertoire of techniques available in evolutionary algorithms with this purpose is fundamental for extending the applicability of these algorithms. In this paper we introduce a unifying information mining approach for evolutionary algorithms. Our proposal is based on a division of the stages where structural modelling of the variables interactions is applied. Particular topological characteristics induced from different stages of the modelling process are identified. Network theory is used to harvest problem structural information from the learned probabilistic graphical models (PGMs). We show how different statistical measures, previously studied for networks from different domains, can be applied to mine the graphical component of PGMs. We provide evidence that the computed measures can be employed for studying problem difficulty, classify ing different problem instances and predicting the algorithm behavior.},\n bibtype = {article},\n author = {Santana, R and Armañanzas, R and Bielza, C and Larrañaga, P},\n journal = {International Journal of Computational Intelligence Systems},\n number = {6}\n}
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\n\n\n
\n Problem domain information extraction is a critical issue in many real-world optimization problems. Increasing the repertoire of techniques available in evolutionary algorithms with this purpose is fundamental for extending the applicability of these algorithms. In this paper we introduce a unifying information mining approach for evolutionary algorithms. Our proposal is based on a division of the stages where structural modelling of the variables interactions is applied. Particular topological characteristics induced from different stages of the modelling process are identified. Network theory is used to harvest problem structural information from the learned probabilistic graphical models (PGMs). We show how different statistical measures, previously studied for networks from different domains, can be applied to mine the graphical component of PGMs. We provide evidence that the computed measures can be employed for studying problem difficulty, classify ing different problem instances and predicting the algorithm behavior.\n
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\n \n\n \n \n \n \n \n The Plackett-Luce Ranking Model on Permutation-based Optimization Problems.\n \n \n \n\n\n \n Ceberio, J.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n In Proceedings of the 2013 IEEE Congress on Evolutionary Computation, pages 494-501, 2013. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {The Plackett-Luce Ranking Model on Permutation-based Optimization Problems},\n type = {inproceedings},\n year = {2013},\n keywords = {isg_ehu},\n pages = {494-501},\n id = {2f2c1dad-41b7-3663-a7e7-32da3e84ff98},\n created = {2021-11-12T08:31:57.327Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:57.327Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Ceberio, Josu and Mendiburu, Alexander and Lozano, Jose A},\n booktitle = {Proceedings of the 2013 IEEE Congress on Evolutionary Computation}\n}
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\n \n\n \n \n \n \n \n Can blood samples be used as surrogate of brain samples in methylation studies?.\n \n \n \n\n\n \n Bediaga, N., G.; Elcoroaristizabal, X.; Calvo, B.; Inza, I.; Pérez, A.; Lozano, J., A.; and de Pancorbo, M., A.\n\n\n \n\n\n\n In Neurogune 2013, 2013. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Can blood samples be used as surrogate of brain samples in methylation studies?},\n type = {inproceedings},\n year = {2013},\n keywords = {isg_ehu},\n id = {38c05b4d-6b1c-3786-af3c-72ecec3f97d6},\n created = {2021-11-12T08:31:59.707Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:59.707Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Bediaga, Naiara G and Elcoroaristizabal, Xabier and Calvo, Borja and Inza, Iñaki and Pérez, Aritz and Lozano, Jose A and de Pancorbo, Maria A},\n booktitle = {Neurogune 2013}\n}
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\n  \n 2012\n \n \n (21)\n \n \n
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\n \n\n \n \n \n \n \n Markov Networks in Evolutionary Computation.\n \n \n \n\n\n \n Santana, R.; and Shakya, S.\n\n\n \n\n\n\n Springer, 2012.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@book{\n title = {Markov Networks in Evolutionary Computation},\n type = {book},\n year = {2012},\n keywords = {isg_ehu},\n publisher = {Springer},\n id = {5f558054-ad8a-3ab0-80be-aa6521ca1e53},\n created = {2021-11-12T08:30:02.149Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:02.149Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n source_type = {book},\n private_publication = {false},\n abstract = {Markov networks and other probabilistic graphical modes have recently received an upsurge in attention from Evolutionary computation community, particularly in the area of Estimation of distribution algorithms (EDAs). EDAs have arisen as one of the most successful experiences in the application of machine learning methods in optimization, mainly due to their efficiency to solve complex real-world optimization problems and their suitability for theoretical analysis. This book focuses on the different steps involved in the conception, implementation and application of EDAs that use Markov networks, and undirected models in general. It can serve as a general introduction to EDAs but covers also an important current void in the study of these algorithms by explaining the specificities and benefits of modeling optimization problems by means of undirected probabilistic models. All major developments to date in the progressive introduction of Markov net works ba sed EDAs are reviewed in the book. Hot current research trends and future perspectives in the enhancement and applicability of EDAs are also covered. The contributions included in the book address topics as relevant as the application of probabilistic-based fitness models, the use of belief propagation algorithms in EDAs and the application of Markov network based EDAs to real-world optimization problems. The book should be of interest to researchers and practitioners from areas such as optimization, evolutionary computation, and machine learning.},\n bibtype = {book},\n author = {Santana, Roberto and Shakya, Siddhartha},\n editor = {Shakya, S and Santana, R}\n}
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\n Markov networks and other probabilistic graphical modes have recently received an upsurge in attention from Evolutionary computation community, particularly in the area of Estimation of distribution algorithms (EDAs). EDAs have arisen as one of the most successful experiences in the application of machine learning methods in optimization, mainly due to their efficiency to solve complex real-world optimization problems and their suitability for theoretical analysis. This book focuses on the different steps involved in the conception, implementation and application of EDAs that use Markov networks, and undirected models in general. It can serve as a general introduction to EDAs but covers also an important current void in the study of these algorithms by explaining the specificities and benefits of modeling optimization problems by means of undirected probabilistic models. All major developments to date in the progressive introduction of Markov net works ba sed EDAs are reviewed in the book. Hot current research trends and future perspectives in the enhancement and applicability of EDAs are also covered. The contributions included in the book address topics as relevant as the application of probabilistic-based fitness models, the use of belief propagation algorithms in EDAs and the application of Markov network based EDAs to real-world optimization problems. The book should be of interest to researchers and practitioners from areas such as optimization, evolutionary computation, and machine learning.\n
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\n \n\n \n \n \n \n \n Using network mesures to test evolved NK-landscapes.\n \n \n \n\n\n \n Santana, R.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n Technical Report Department of Computer Science and Artificial Intelligence, University of the Basque Country, 2012.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@techreport{\n title = {Using network mesures to test evolved NK-landscapes},\n type = {techreport},\n year = {2012},\n keywords = {isg_ehu,isg_jcr},\n issue = {EHU-KZAA-TR:2012-03},\n institution = {Department of Computer Science and Artificial Intelligence, University of the Basque Country},\n id = {4049103a-8e48-3680-83f2-751a2c1d3f9a},\n created = {2021-11-12T08:30:14.316Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:14.316Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {techreport},\n private_publication = {false},\n bibtype = {techreport},\n author = {Santana, R and Mendiburu, A and Lozano, J A}\n}
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\n \n\n \n \n \n \n \n A review on probabilistic graphical models in evolutionary computation.\n \n \n \n\n\n \n Larrañaga, P.; Karshenas, H.; Bielza, C.; and Santana, R.\n\n\n \n\n\n\n Journal of Heuristics, 18(5): 795-819. 2012.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {A review on probabilistic graphical models in evolutionary computation},\n type = {article},\n year = {2012},\n keywords = {isg_ehu,isg_jcr},\n pages = {795-819},\n volume = {18},\n id = {8f376eed-ed7c-334f-977f-9510dd1a751e},\n created = {2021-11-12T08:30:28.875Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:28.875Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {Thanks to their inherent properties, probabilistic graphical models are one of the prime candidates for machine learning and decision making tasks especially in uncertain domains. Their capabilities, like representation, inference and learning, if used effectively, can greatly help to build intelligent systems that are able to act accordingly in different problem domains. Evolutionary algorithms is one such discipline that has employed probabilistic graphical models to improve the search for optimal solutions in complex problems. This paper shows how probabilistic graphical models have been used in evolutionary algorithms to improve their performance in solving complex problems. Specifically, we give a survey of probabilistic model building-based evolutionary algorithms, called estimation of distribution algorithms, and compare different methods for probabilistic modeling in these algorithms.},\n bibtype = {article},\n author = {Larrañaga, P and Karshenas, H and Bielza, C and Santana, R},\n journal = {Journal of Heuristics},\n number = {5}\n}
\n
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\n Thanks to their inherent properties, probabilistic graphical models are one of the prime candidates for machine learning and decision making tasks especially in uncertain domains. Their capabilities, like representation, inference and learning, if used effectively, can greatly help to build intelligent systems that are able to act accordingly in different problem domains. Evolutionary algorithms is one such discipline that has employed probabilistic graphical models to improve the search for optimal solutions in complex problems. This paper shows how probabilistic graphical models have been used in evolutionary algorithms to improve their performance in solving complex problems. Specifically, we give a survey of probabilistic model building-based evolutionary algorithms, called estimation of distribution algorithms, and compare different methods for probabilistic modeling in these algorithms.\n
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\n \n\n \n \n \n \n \n A review on Estimation of Distribution Algorithms in Permutation-based Combinatorial Optimization Problems.\n \n \n \n\n\n \n Ceberio, J.; Irurozki, E.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n Progress in Artificial Intelligence, 1(1): 103-117. 2012.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {A review on Estimation of Distribution Algorithms in Permutation-based Combinatorial Optimization Problems},\n type = {article},\n year = {2012},\n keywords = {isg_ehu,isg_jcr},\n pages = {103-117},\n volume = {1},\n id = {c3fd83b1-3006-3d0e-b15a-edda8001a813},\n created = {2021-11-12T08:30:29.138Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:29.138Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Ceberio, Josu and Irurozki, Ekhine and Mendiburu, Alexander and Lozano, Jose A},\n journal = {Progress in Artificial Intelligence},\n number = {1}\n}
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\n \n\n \n \n \n \n \n Multi-objective optimization based on joint probabilistic modeling of objectives and variables.\n \n \n \n\n\n \n Karshenas, H.; Santana, R.; Bielza, C.; and Larrañaga, P.\n\n\n \n\n\n\n Technical Report Department of Artificial Intelligence, Faculty of Informatics, Technical University of Madrid, 2012.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@techreport{\n title = {Multi-objective optimization based on joint probabilistic modeling of objectives and variables},\n type = {techreport},\n year = {2012},\n keywords = {isg_ehu,isg_jcr},\n issue = {UPM-FI/DIA/2012-2},\n institution = {Department of Artificial Intelligence, Faculty of Informatics, Technical University of Madrid},\n id = {768d48be-cf38-3a86-aae8-138cc98d35c4},\n created = {2021-11-12T08:30:29.903Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:29.903Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {techreport},\n private_publication = {false},\n abstract = {This paper proposes a new multi-objective estimation of distribution algorithm (EDA) based on joint modeling of objectives and variables. This EDA uses the multi-dimensional Bayesian network as its probabilistic model. In this way it can capture the dependencies between objectives, variables and objectives, as well as the dependencies learnt between variables in other Bayesian network-based EDAs. This model leads to a problem decomposition that helps the proposed algorithm to find better trade-off solutions to the multi-objective problem. In addition to Pareto set approximation, the algorithm is also able to estimate the structure of the multi-objective problem. To apply the algorithm to many-objective problems, the algorithm includes four different ranking methods proposed in the literature for this purpose. The algorithm is applied to the set of walking fish group (WFG) problems, and its optimization performance is compared with an evolutionary algorit hm and another multi-objective EDA. The experimental results show that the proposed algorithm performs significantly better on many of the problems and for different objective space dimensions, and achieves comparable results on some compared with the other algorithms.},\n bibtype = {techreport},\n author = {Karshenas, H and Santana, R and Bielza, C and Larrañaga, P}\n}
\n
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\n This paper proposes a new multi-objective estimation of distribution algorithm (EDA) based on joint modeling of objectives and variables. This EDA uses the multi-dimensional Bayesian network as its probabilistic model. In this way it can capture the dependencies between objectives, variables and objectives, as well as the dependencies learnt between variables in other Bayesian network-based EDAs. This model leads to a problem decomposition that helps the proposed algorithm to find better trade-off solutions to the multi-objective problem. In addition to Pareto set approximation, the algorithm is also able to estimate the structure of the multi-objective problem. To apply the algorithm to many-objective problems, the algorithm includes four different ranking methods proposed in the literature for this purpose. The algorithm is applied to the set of walking fish group (WFG) problems, and its optimization performance is compared with an evolutionary algorit hm and another multi-objective EDA. The experimental results show that the proposed algorithm performs significantly better on many of the problems and for different objective space dimensions, and achieves comparable results on some compared with the other algorithms.\n
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\n \n\n \n \n \n \n \n Identification of a biomarker panel for colorectal cancer diagnosis.\n \n \n \n\n\n \n Garcia-Bilbao, A.; Armañanzas, R.; Ispizua, Z.; Calvo, B.; Alonso-Varona, A.; Inza, I.; Larrañaga, P.; Vivanco, G., L.; Suarez-Merino, B.; and Betanzos, M.\n\n\n \n\n\n\n BMC Cancer, 12(43). 2012.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Identification of a biomarker panel for colorectal cancer diagnosis.},\n type = {article},\n year = {2012},\n keywords = {isg_ehu,isg_jcr},\n volume = {12},\n id = {ce5c6cfb-494e-30ad-89d2-bba608a97b4c},\n created = {2021-11-12T08:30:30.484Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:30.484Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Garcia-Bilbao, Amaia and Armañanzas, Rubén and Ispizua, Z and Calvo, Begoña and Alonso-Varona, A and Inza, Iñaki and Larrañaga, Pedro and Vivanco, G Lopez and Suarez-Merino, B and Betanzos, M},\n journal = {BMC Cancer},\n number = {43}\n}
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\n \n\n \n \n \n \n \n Introducing the use of model-based evolutionary algorithms for EEG-based motor imagery classification.\n \n \n \n\n\n \n Santana, R.; Bonnet, L.; Légeny, J.; and Lécuyer, A.\n\n\n \n\n\n\n In Proceedings of the 2012 Genetic and Evolutionary Computation Conference GECCO-2012, pages 1159-1166, 2012. ACM Press\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Introducing the use of model-based evolutionary algorithms for EEG-based motor imagery classification},\n type = {inproceedings},\n year = {2012},\n keywords = {isg_ehu},\n pages = {1159-1166},\n publisher = {ACM Press},\n id = {4c24cfb9-262f-33c9-9dc6-f296398637c6},\n created = {2021-11-12T08:30:42.505Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:42.505Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {Brain computer interfaces (BCIs) allow the direct human-computer interaction without the need of motor intervention. To properly and efficiently decode brain signals into computer commands the application of machine-learning techniques is required. Evolutionary algorithms have been increasingly applied in different steps of BCI implementations. In this paper we introduce the use of the covariance matrix adaptation evolution strategy (CMA-ES) for BCI systems based on motor imagery. The optimization algorithm is used to evolve linear classifiers able to outperform other traditional classifiers. We also analyze the role of modeling variables interactions for additional insight in the understanding of the BCI paradigms.},\n bibtype = {inproceedings},\n author = {Santana, R and Bonnet, L and Légeny, J and Lécuyer, A},\n booktitle = {Proceedings of the 2012 Genetic and Evolutionary Computation Conference GECCO-2012}\n}
\n
\n\n\n
\n Brain computer interfaces (BCIs) allow the direct human-computer interaction without the need of motor intervention. To properly and efficiently decode brain signals into computer commands the application of machine-learning techniques is required. Evolutionary algorithms have been increasingly applied in different steps of BCI implementations. In this paper we introduce the use of the covariance matrix adaptation evolution strategy (CMA-ES) for BCI systems based on motor imagery. The optimization algorithm is used to evolve linear classifiers able to outperform other traditional classifiers. We also analyze the role of modeling variables interactions for additional insight in the understanding of the BCI paradigms.\n
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\n \n\n \n \n \n \n \n Contributions to the Analysis and Understanding of Estimation of Distribution Algorithms.\n \n \n \n\n\n \n Echegoyen, C.\n\n\n \n\n\n\n Ph.D. Thesis, 2012.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@phdthesis{\n title = {Contributions to the Analysis and Understanding of Estimation of Distribution Algorithms},\n type = {phdthesis},\n year = {2012},\n institution = {University of the Basque Country},\n id = {a882264f-413d-3df3-b35b-664bd53e0235},\n created = {2021-11-12T08:30:50.685Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:50.685Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {phdthesis},\n private_publication = {false},\n bibtype = {phdthesis},\n author = {Echegoyen, Carlos}\n}
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\n \n\n \n \n \n \n \n Structural transfer using EDAs: An application to multi-marker tagging SNP selection.\n \n \n \n\n\n \n Santana, R.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n In Proceedings of the 2012 Congress on Evolutionary Computation CEC-2012, pages 3484-3491, 2012. IEEE Press\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Structural transfer using EDAs: An application to multi-marker tagging SNP selection},\n type = {inproceedings},\n year = {2012},\n keywords = {isg_ehu},\n pages = {3484-3491},\n publisher = {IEEE Press},\n id = {80740b97-465a-3fb1-8585-ac0dce996088},\n created = {2021-11-12T08:31:01.898Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:01.898Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {In this paper we investigate the question of transfer learning in evolutionary optimization using estimation of distribution algorithms. We propose a framework for transfer learning between related optimization problems by means of structural transfer. Different methods for incrementing or replacing the (possibly unavailable) structural information of the target optimization problem are presented. As a test case we solve the multi-marker tagging single-nucleotide polymorphism (SNP) selection problem, a real world problem from genetics. The introduced variants of structural transfer are validated in the computation of tagging SNPs on a database of 1167 individuals from 58 human populations worldwide. Our experimental results show significant improvements over EDAs that do not incorporate information from related problems.},\n bibtype = {inproceedings},\n author = {Santana, R and Mendiburu, A and Lozano, J A},\n booktitle = {Proceedings of the 2012 Congress on Evolutionary Computation CEC-2012}\n}
\n
\n\n\n
\n In this paper we investigate the question of transfer learning in evolutionary optimization using estimation of distribution algorithms. We propose a framework for transfer learning between related optimization problems by means of structural transfer. Different methods for incrementing or replacing the (possibly unavailable) structural information of the target optimization problem are presented. As a test case we solve the multi-marker tagging single-nucleotide polymorphism (SNP) selection problem, a real world problem from genetics. The introduced variants of structural transfer are validated in the computation of tagging SNPs on a database of 1167 individuals from 58 human populations worldwide. Our experimental results show significant improvements over EDAs that do not incorporate information from related problems.\n
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\n \n\n \n \n \n \n \n A Markovianity based optimisation algorithm.\n \n \n \n\n\n \n Shakya, S.; Santana, R.; and Lozano, J., A.\n\n\n \n\n\n\n Genetic Programming and Evolvable Machines, 13(2): 159-195. 2012.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {A Markovianity based optimisation algorithm},\n type = {article},\n year = {2012},\n keywords = {isg_ehu,isg_jcr},\n pages = {159-195},\n volume = {13},\n id = {cc67144f-3433-3558-bb79-34ef48cb2981},\n created = {2021-11-12T08:31:10.962Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:10.962Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {Several Estimation of Distribution Algorithms (EDAs) based on Markov networks have been recently proposed. The key idea behind these EDAs was to factorise the joint probability distribution of solution variables in terms of cliques in the undirected graph. As such, they made use of the global Markov property of the Markov network in one form or another. This paper presents a Markov Network based EDA that is based on the use of the local Markov property, the Markovianity, and does not directly model the joint distribution. We call it Markovianity based Optimisation Algorithm. The algorithm combines a novel method for extracting the neighbourhood structure from the mutual information between the variables, with a Gibbs sampler method to generate new points. We present an extensive empirical validation of the algorithm on problems with complex interactions, comparing its performance with other EDAs that use higher order interactions. We extend the a nalysis to other functions with discrete representation, where EDA results are scarce, comparing the algorithm with state of the art EDAs that use marginal product factorisations.},\n bibtype = {article},\n author = {Shakya, S and Santana, R and Lozano, J A},\n journal = {Genetic Programming and Evolvable Machines},\n number = {2}\n}
\n
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\n Several Estimation of Distribution Algorithms (EDAs) based on Markov networks have been recently proposed. The key idea behind these EDAs was to factorise the joint probability distribution of solution variables in terms of cliques in the undirected graph. As such, they made use of the global Markov property of the Markov network in one form or another. This paper presents a Markov Network based EDA that is based on the use of the local Markov property, the Markovianity, and does not directly model the joint distribution. We call it Markovianity based Optimisation Algorithm. The algorithm combines a novel method for extracting the neighbourhood structure from the mutual information between the variables, with a Gibbs sampler method to generate new points. We present an extensive empirical validation of the algorithm on problems with complex interactions, comparing its performance with other EDAs that use higher order interactions. We extend the a nalysis to other functions with discrete representation, where EDA results are scarce, comparing the algorithm with state of the art EDAs that use marginal product factorisations.\n
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\n \n\n \n \n \n \n \n Regularized logistic regression and multi-objective variable selection for classifying MEG data.\n \n \n \n\n\n \n Santana, R.; Bielza, C.; and Larrañaga, P.\n\n\n \n\n\n\n Biological Cybernetics, 106(6-7): 389-405. 2012.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Regularized logistic regression and multi-objective variable selection for classifying MEG data},\n type = {article},\n year = {2012},\n keywords = {isg_ehu,isg_jcr},\n pages = {389-405},\n volume = {106},\n id = {7e0c4a41-21c6-31ca-9b46-3df30b676b06},\n created = {2021-11-12T08:31:17.455Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:17.455Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {This paper addresses the question of maximizing classifier accuracy for classifying task-related mental activity from Magnetoencelophalography (MEG) data. We propose the use of different sources of information and introduce an automatic channel selection procedure. To determine an informative set of channels, our approach combines a variety of machine learning algorithms: feature subset selection methods, classifiers based on regularized logistic regression, information fusion, and multiobjective optimization based on probabilistic modeling of the search space. The experimental results show that our proposal is able to improve classification accuracy compared to approaches whose classifiers use only one type of MEG information or for which the set of channels is fixed a priori.},\n bibtype = {article},\n author = {Santana, R and Bielza, C and Larrañaga, P},\n journal = {Biological Cybernetics},\n number = {6-7}\n}
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\n This paper addresses the question of maximizing classifier accuracy for classifying task-related mental activity from Magnetoencelophalography (MEG) data. We propose the use of different sources of information and introduce an automatic channel selection procedure. To determine an informative set of channels, our approach combines a variety of machine learning algorithms: feature subset selection methods, classifiers based on regularized logistic regression, information fusion, and multiobjective optimization based on probabilistic modeling of the search space. The experimental results show that our proposal is able to improve classification accuracy compared to approaches whose classifiers use only one type of MEG information or for which the set of channels is fixed a priori.\n
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\n \n\n \n \n \n \n \n Wrapper positive Bayesian network classifiers.\n \n \n \n\n\n \n Calvo, B.; Inza, I.; Larrañaga, P.; and Lozano, J., A.\n\n\n \n\n\n\n Knowledge and information systems, 33(3): 631-654. 2012.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Wrapper positive Bayesian network classifiers},\n type = {article},\n year = {2012},\n keywords = {isg_ehu,isg_jcr},\n pages = {631-654},\n volume = {33},\n publisher = {Springer-Verlag},\n id = {e336bcaf-1fdb-3caa-8359-aa99b5070a1a},\n created = {2021-11-12T08:31:18.240Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:18.240Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Calvo, Borja and Inza, Iñaki and Larrañaga, Pedro and Lozano, Jose A},\n journal = {Knowledge and information systems},\n number = {3}\n}
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\n \n\n \n \n \n \n \n Managing Burstiness and Scalability in Event-Driven Models on the SpiNNaker Neuromimetic System.\n \n \n \n\n\n \n Rast, A.; Navaridas, J.; Jin, X.; Galluppi, F.; Plana, L.; Miguel-Alonso, J.; Patterson, C.; Luján, M.; and Furber, S.\n\n\n \n\n\n\n International Journal of Parallel Programming, 40(6): 553-582. 2012.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Managing Burstiness and Scalability in Event-Driven Models on the SpiNNaker Neuromimetic System},\n type = {article},\n year = {2012},\n keywords = {isg_ehu,isg_jcr},\n pages = {553-582},\n volume = {40},\n id = {58053853-a25d-30ab-b775-18375924e241},\n created = {2021-11-12T08:31:27.125Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:27.125Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Rast, Alexander and Navaridas, Javier and Jin, Xin and Galluppi, Francesco and Plana, Luis and Miguel-Alonso, José and Patterson, Cameron and Luján, Mikel and Furber, Stephen},\n journal = {International Journal of Parallel Programming},\n number = {6}\n}
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\n \n\n \n \n \n \n \n Toward Understanding EDAs Based on Bayesian Networks Through a Quantitative Analysis.\n \n \n \n\n\n \n Echegoyen, C.; Mendiburu, A.; Santana, R.; and Lozano, J., A.\n\n\n \n\n\n\n IEEE Transactions on Evolutionary Computation, 16(2): 173-189. 2012.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Toward Understanding EDAs Based on Bayesian Networks Through a Quantitative Analysis},\n type = {article},\n year = {2012},\n keywords = {isg_ehu,isg_jcr},\n pages = {173-189},\n volume = {16},\n id = {cb3c1f8e-c9c1-3d8f-b8d9-2728da776e33},\n created = {2021-11-12T08:31:27.452Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:27.452Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {The successful application of estimation of distribution algorithms (EDAs) to solve different kinds of problems has reinforced their candidature as promising black-box optimization tools. However, their internal behavior is still not completely understood and therefore it is necessary to work in this direction in order to advance their development. This paper presents a methodology of analysis which provides new information about the behavior of EDAs by quantitatively analyzing the probabilistic models learned during the search. We particularly focus on calculating the probabilities of the optimal solutions, the most probable solution given by the model and the best individual of the population at each step of the algorithm. We carry out the analysis by optimizing functions of different nature such as Trap5, two variants of Ising spin glass and Max-SAT. By using different structures in the probabilistic models, we also analyze the impact of the s tructura l model accuracy in the quantitative behavior of EDAs. In addition, the objective function values of our analyzed key solutions are contrasted with their probability values in order to study the connection between function and probabilistic models. The results not only show information about the internal behavior of EDAs, but also about the quality of the optimization process and setup of the parameters, the relationship between the probabilistic model and the fitness function, and even about the problem itself. Furthermore, the results allow us to discover common patterns of behavior in EDAs and propose new ideas in the development of this type of algorithms.},\n bibtype = {article},\n author = {Echegoyen, C and Mendiburu, A and Santana, R and Lozano, J A},\n journal = {IEEE Transactions on Evolutionary Computation},\n number = {2}\n}
\n
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\n The successful application of estimation of distribution algorithms (EDAs) to solve different kinds of problems has reinforced their candidature as promising black-box optimization tools. However, their internal behavior is still not completely understood and therefore it is necessary to work in this direction in order to advance their development. This paper presents a methodology of analysis which provides new information about the behavior of EDAs by quantitatively analyzing the probabilistic models learned during the search. We particularly focus on calculating the probabilities of the optimal solutions, the most probable solution given by the model and the best individual of the population at each step of the algorithm. We carry out the analysis by optimizing functions of different nature such as Trap5, two variants of Ising spin glass and Max-SAT. By using different structures in the probabilistic models, we also analyze the impact of the s tructura l model accuracy in the quantitative behavior of EDAs. In addition, the objective function values of our analyzed key solutions are contrasted with their probability values in order to study the connection between function and probabilistic models. The results not only show information about the internal behavior of EDAs, but also about the quality of the optimization process and setup of the parameters, the relationship between the probabilistic model and the fitness function, and even about the problem itself. Furthermore, the results allow us to discover common patterns of behavior in EDAs and propose new ideas in the development of this type of algorithms.\n
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\n \n\n \n \n \n \n \n An analysis of the use of probabilistic modeling for synaptic connectivity prediction from genomic data.\n \n \n \n\n\n \n Santana, R.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n In Proceedings of the 2012 Congress on Evolutionary Computation CEC-2012, pages 3221-3228, 2012. IEEE Press\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {An analysis of the use of probabilistic modeling for synaptic connectivity prediction from genomic data},\n type = {inproceedings},\n year = {2012},\n keywords = {isg_ehu},\n pages = {3221-3228},\n publisher = {IEEE Press},\n id = {118d499f-19ac-34ad-a85f-b4c86179a1e9},\n created = {2021-11-12T08:31:28.962Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:28.962Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {The identification of the specific genes that influence particular phenotypes is a common problem in genetic studies. In this paper we address the problem of determining the influence of gene joint expression in synapse predictability. The question is posed as an optimization problem in which the conditional entropy of gene subsets with respect to the synaptic connectivity phenotype is minimized. We investigate the use of single- and multi-objective estimation of distribution algorithms and focus on real data from C. elegans synaptic connectivity. We show that the introduced algorithms are able to compute gene sets that allow an accurate synapse predictability. However, the multi-objective approach can simultaneously search for gene sets with different number of genes. Our results also indicate that optimization problems defined on constrained binary spaces remain challenging for the conception of competitive estimation of distribution algorithm.},\n bibtype = {inproceedings},\n author = {Santana, R and Mendiburu, A and Lozano, J A},\n booktitle = {Proceedings of the 2012 Congress on Evolutionary Computation CEC-2012}\n}
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\n The identification of the specific genes that influence particular phenotypes is a common problem in genetic studies. In this paper we address the problem of determining the influence of gene joint expression in synapse predictability. The question is posed as an optimization problem in which the conditional entropy of gene subsets with respect to the synaptic connectivity phenotype is minimized. We investigate the use of single- and multi-objective estimation of distribution algorithms and focus on real data from C. elegans synaptic connectivity. We show that the introduced algorithms are able to compute gene sets that allow an accurate synapse predictability. However, the multi-objective approach can simultaneously search for gene sets with different number of genes. Our results also indicate that optimization problems defined on constrained binary spaces remain challenging for the conception of competitive estimation of distribution algorithm.\n
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\n \n\n \n \n \n \n \n Conductance interaction identification by means of Boltzmann distribution and mutual information analysis in conductance-based neuron models.\n \n \n \n\n\n \n Santana, R.; Bielza, C.; and Larrañaga, P.\n\n\n \n\n\n\n BMC Neuroscience, 13(Suppl 1). 2012.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Conductance interaction identification by means of Boltzmann distribution and mutual information analysis in conductance-based neuron models},\n type = {article},\n year = {2012},\n keywords = {isg_ehu,isg_jcr},\n volume = {13},\n publisher = {BioMed Central},\n id = {da2554fe-947f-37f3-bc06-a00a821deb0c},\n created = {2021-11-12T08:31:33.303Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:33.303Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {In this paper we propose a probabilistic approach based on the computation of the Boltzmann distribution and the mutual information of conductance interactions to learn higher-order, not necessarily pair-wise, potential co-regulation mechanisms from a database of the crustacean stomatogastric ganglion pyloric circuit models.},\n bibtype = {article},\n author = {Santana, R and Bielza, C and Larrañaga, P},\n journal = {BMC Neuroscience},\n number = {Suppl 1}\n}
\n
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\n In this paper we propose a probabilistic approach based on the computation of the Boltzmann distribution and the mutual information of conductance interactions to learn higher-order, not necessarily pair-wise, potential co-regulation mechanisms from a database of the crustacean stomatogastric ganglion pyloric circuit models.\n
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\n \n\n \n \n \n \n \n New methods for generating populations in Markov network based EDAs: Decimation strategies and model-based template recombination.\n \n \n \n\n\n \n Santana, R.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n Technical Report Department of Computer Science and Artificial Intelligence, University of the Basque Country, 2012.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@techreport{\n title = {New methods for generating populations in Markov network based EDAs: Decimation strategies and model-based template recombination},\n type = {techreport},\n year = {2012},\n keywords = {isg_ehu,isg_jcr},\n issue = {EHU-KZAA-TR:2012-05},\n institution = {Department of Computer Science and Artificial Intelligence, University of the Basque Country},\n id = {ca5cc796-c429-344c-83b9-6a59c838a17f},\n created = {2021-11-12T08:31:34.484Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:34.484Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {techreport},\n private_publication = {false},\n abstract = {Methods for generating a new population are a fundamental component of estimation of distribution algorithms (EDAs). They serve to transfer the information contained in the probabilistic model to the new generated population. In EDAs based on Markov networks, methods for generating new populations usually discard information contained in the model to gain in efficiency. Other methods like Gibbs sampling use information about all interactions in the model but are computationally very costly. In this paper we propose new methods for generating new solutions in EDAs based on Markov networks. We introduce approaches based on inference methods for computing the most probable configurations and model-based template recombination. We show that the application of different variants of inference methods can increase the EDAs convergence rate and reduce the number of function evaluations needed to find the optimum of binary and non-binary discrete function s.},\n bibtype = {techreport},\n author = {Santana, R and Mendiburu, A and Lozano, J A}\n}
\n
\n\n\n
\n Methods for generating a new population are a fundamental component of estimation of distribution algorithms (EDAs). They serve to transfer the information contained in the probabilistic model to the new generated population. In EDAs based on Markov networks, methods for generating new populations usually discard information contained in the model to gain in efficiency. Other methods like Gibbs sampling use information about all interactions in the model but are computationally very costly. In this paper we propose new methods for generating new solutions in EDAs based on Markov networks. We introduce approaches based on inference methods for computing the most probable configurations and model-based template recombination. We show that the application of different variants of inference methods can increase the EDAs convergence rate and reduce the number of function evaluations needed to find the optimum of binary and non-binary discrete function s.\n
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\n \n\n \n \n \n \n \n Maximizing the number of polychronous groups in spiking networks.\n \n \n \n\n\n \n Santana, R.; Bielza, C.; and Larrañaga, P.\n\n\n \n\n\n\n In Companion Proceedings of the 2012 Genetic and Evolutionary Computation Conference GECCO-2012, pages 1499-1500, 2012. ACM Press\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Maximizing the number of polychronous groups in spiking networks},\n type = {inproceedings},\n year = {2012},\n keywords = {isg_ehu},\n pages = {1499-1500},\n publisher = {ACM Press},\n id = {680ef9e6-7512-3319-8c7d-4074db190bbd},\n created = {2021-11-12T08:31:53.596Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:53.596Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {In this paper we investigate the effect of biasing the axonal connection delay values in the number of polychronous groups produced for a spiking neuron network model. We use an estimation of distribution algorithm (EDA) that learns tree models to search for optimal delay configurations. Our results indicate that the introduced approach can be used to considerably increase the number of such groups.},\n bibtype = {inproceedings},\n author = {Santana, R and Bielza, C and Larrañaga, P},\n booktitle = {Companion Proceedings of the 2012 Genetic and Evolutionary Computation Conference GECCO-2012}\n}
\n
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\n In this paper we investigate the effect of biasing the axonal connection delay values in the number of polychronous groups produced for a spiking neuron network model. We use an estimation of distribution algorithm (EDA) that learns tree models to search for optimal delay configurations. Our results indicate that the introduced approach can be used to considerably increase the number of such groups.\n
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\n \n\n \n \n \n \n \n Evolving NK-complexity for evolutionary solvers.\n \n \n \n\n\n \n Santana, R.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n In Companion Proceedings of the 2012 Genetic and Evolutionary Computation Conference GECCO-2012, pages 1473-1474, 2012. ACM Press\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Evolving NK-complexity for evolutionary solvers},\n type = {inproceedings},\n year = {2012},\n keywords = {isg_ehu},\n pages = {1473-1474},\n publisher = {ACM Press},\n id = {29728fa3-f523-3a47-bebb-f3b1eee3e8ec},\n created = {2021-11-12T08:31:57.883Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:57.883Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {In this paper we empirically investigate the structural characteristics that can help to predict the complexity of NK-landscape instances for estimation of distribution algorithms (EDAs). We evolve instances that maximize the EDA complexity in terms of its success rate. Similarly, instances that minimize the algorithm complexity are evolved. We then identify network measures, computed from the structures of the NK-landscape instances, that have a statistically significant difference between the set of easy and hard instances. The features identified are consistently significant for different values of N and K.},\n bibtype = {inproceedings},\n author = {Santana, R and Mendiburu, A and Lozano, J A},\n booktitle = {Companion Proceedings of the 2012 Genetic and Evolutionary Computation Conference GECCO-2012}\n}
\n
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\n In this paper we empirically investigate the structural characteristics that can help to predict the complexity of NK-landscape instances for estimation of distribution algorithms (EDAs). We evolve instances that maximize the EDA complexity in terms of its success rate. Similarly, instances that minimize the algorithm complexity are evolved. We then identify network measures, computed from the structures of the NK-landscape instances, that have a statistically significant difference between the set of easy and hard instances. The features identified are consistently significant for different values of N and K.\n
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\n \n\n \n \n \n \n \n Clases de equivalencia en algoritmos de estimación de distribuciones.\n \n \n \n\n\n \n Echegoyen, C.; Mendiburu, A.; Santana, R.; and Lozano, J., A.\n\n\n \n\n\n\n In Proceedings of the VIII Congreso Español sobre Metaheur\\'\\isticas, Algoritmos Evolutivos y Bioinspirados (MAEB-2012), 2012. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Clases de equivalencia en algoritmos de estimación de distribuciones},\n type = {inproceedings},\n year = {2012},\n keywords = {isg_ehu},\n id = {cbb28f8c-7683-3ee5-afd3-f0889599010a},\n created = {2021-11-12T08:32:00.610Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:00.610Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {Entender la relación que surge entre un algoritmo de búsqueda y el espacio de problemas es una cuestión fundamental en el campo de la optimización. En este trabajo nos centramos en la elaboración de taxonom\\'\\ias de problemas para algoritmos de est de distribuciones (EDAs). Mediante la utilización del modelo de población infinita y asumiendo selección basada en el ranqueo de las soluciones, agrupamos las funciones inyectivas segun el comportamiento del EDA. Para llevar a cabo esta clasificación, se define una relación de equivalencia entre funciones que permite particionar el espacio de funciones en clases de equivalencia para las cuales el algoritmo tiene un comportamiento similar. Considerar diferentes modelos probabil\\'\\isticos en el EDA genera diferentes particiones del conjunto de posibles problemas. Como consecuencia natural de las definiciones, todas las funciones objetivo están en la misma clase de equivale ncia cua ndo el algoritmo no impone restricciones sobre el modelo probabil\\'\\istico. Con el fin de crear una primera taxonom\\'\\ia de problemas, nos centramos en la partición que se produce cuando se considera un modelo probabil\\'\\istico que asume independencia entre las variables. Para ello, primero fijamos las condiciones suficientes para decidir si dos funciones son equivalentes y segundo, obtenemos los operadores para describir y contar los miembros de una clase. En general, el presente trabajo sienta las bases para continuar el estudio del comportamiento de los EDAs y su relación con los problemas de optimización.},\n bibtype = {inproceedings},\n author = {Echegoyen, C and Mendiburu, A and Santana, R and Lozano, J A},\n booktitle = {Proceedings of the VIII Congreso Español sobre Metaheur\\'\\isticas, Algoritmos Evolutivos y Bioinspirados (MAEB-2012)}\n}
\n
\n\n\n
\n Entender la relación que surge entre un algoritmo de búsqueda y el espacio de problemas es una cuestión fundamental en el campo de la optimización. En este trabajo nos centramos en la elaboración de taxonom\\'\\ias de problemas para algoritmos de est de distribuciones (EDAs). Mediante la utilización del modelo de población infinita y asumiendo selección basada en el ranqueo de las soluciones, agrupamos las funciones inyectivas segun el comportamiento del EDA. Para llevar a cabo esta clasificación, se define una relación de equivalencia entre funciones que permite particionar el espacio de funciones en clases de equivalencia para las cuales el algoritmo tiene un comportamiento similar. Considerar diferentes modelos probabil\\'\\isticos en el EDA genera diferentes particiones del conjunto de posibles problemas. Como consecuencia natural de las definiciones, todas las funciones objetivo están en la misma clase de equivale ncia cua ndo el algoritmo no impone restricciones sobre el modelo probabil\\'\\istico. Con el fin de crear una primera taxonom\\'\\ia de problemas, nos centramos en la partición que se produce cuando se considera un modelo probabil\\'\\istico que asume independencia entre las variables. Para ello, primero fijamos las condiciones suficientes para decidir si dos funciones son equivalentes y segundo, obtenemos los operadores para describir y contar los miembros de una clase. En general, el presente trabajo sienta las bases para continuar el estudio del comportamiento de los EDAs y su relación con los problemas de optimización.\n
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\n \n\n \n \n \n \n \n \n An interactive optimization approach to a real-world oceanographic campaign planning problem.\n \n \n \n \n\n\n \n Ibarbia, I.; Mendiburu, A.; Santos, M.; and Lozano, J., A.\n\n\n \n\n\n\n Appl. Intell., 36(3): 721-734. 2012.\n \n\n\n\n
\n\n\n\n \n \n \"AnWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {An interactive optimization approach to a real-world oceanographic campaign planning problem},\n type = {article},\n year = {2012},\n keywords = {isg_ehu,isg_jcr},\n pages = {721-734},\n volume = {36},\n websites = {http://dx.doi.org/10.1007/s10489-011-0291-2},\n id = {12632fa8-b13d-38a9-bbb5-fcefe98bbc1e},\n created = {2021-11-12T08:32:16.998Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:16.998Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Ibarbia, Izaskun and Mendiburu, Alexander and Santos, Maria and Lozano, José A},\n doi = {10.1007/s10489-011-0291-2},\n journal = {Appl. Intell.},\n number = {3}\n}
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\n  \n 2011\n \n \n (28)\n \n \n
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\n \n\n \n \n \n \n \n Approaching Sentiment Analysis by Using Semi-supervised Learning of Multi-dimensional Classifiers.\n \n \n \n\n\n \n Ortigosa-Hernández, J.; Rodr\\'\\iguez, J., D.; Alzate, L.; Inza, I.; Lozano, J., A.; Lucania, M.; Inza, I.; and Lozano, J., A.\n\n\n \n\n\n\n 2011.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@misc{\n title = {Approaching Sentiment Analysis by Using Semi-supervised Learning of Multi-dimensional Classifiers},\n type = {misc},\n year = {2011},\n source = {Neurocomputing},\n keywords = {isg_ehu,isg_jcr},\n pages = {98-115},\n volume = {92},\n issue = {EHU-KZAA-TR-4-2011},\n institution = {Department of Computer Science and Artificial Intelligence, University of the Basque Country, San Sebastián, Spain},\n id = {e9b2250e-82f6-3139-836c-fb09ec93f956},\n created = {2021-11-12T08:30:02.989Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:02.989Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n bibtype = {misc},\n author = {Ortigosa-Hernández, J and Rodr\\'\\iguez, J D and Alzate, L and Inza, I and Lozano, J A and Lucania, M and Inza, I and Lozano, J A}\n}
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\n \n\n \n \n \n \n \n Learning Probability Distributions over Permutations by Means of Fourier Coefficients.\n \n \n \n\n\n \n Irurozki, E.; Calvo, B.; and Lozano, J., A.\n\n\n \n\n\n\n In Canadian Conference on AI, volume 6657, of Lecture Notes in Computer Science, pages 186-191, 2011. Springer-Verlag\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Learning Probability Distributions over Permutations by Means of Fourier Coefficients.},\n type = {inproceedings},\n year = {2011},\n keywords = {dblp,isg_,isg_ehu},\n pages = {186-191},\n volume = {6657},\n publisher = {Springer-Verlag},\n series = {Lecture Notes in Computer Science},\n id = {de1d6010-e633-371f-ba7d-9f6636d95f72},\n created = {2021-11-12T08:30:06.190Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:06.190Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Irurozki, Ekhine and Calvo, Borja and Lozano, Jose A},\n booktitle = {Canadian Conference on AI}\n}
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\n \n\n \n \n \n \n \n A preprocessing procedure for haplotype inference by pure parsimony.\n \n \n \n\n\n \n Irurozki, E.; Calvo, B.; and Lozano, J., A.\n\n\n \n\n\n\n IEEE/ACM Transactions on Computational Biology and Bioinformatics, 8(5): 1183-1195. 2011.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {A preprocessing procedure for haplotype inference by pure parsimony},\n type = {article},\n year = {2011},\n keywords = {Biology and genetics,haplotype inference,optimization},\n pages = {1183-1195},\n volume = {8},\n id = {30f4af0e-15d1-395e-bcad-29c83fe1f3f9},\n created = {2021-11-12T08:30:07.872Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:07.872Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {Haplotype data is especially important in the study of complex diseases since it contains more information than genotype data. However, obtaining haplotype data is technically difficult and expensive. Computational methods have proved to be an effective way of inferring haplotype data from genotype data. One of these methods, the haplotype inference by pure parsimony approach (HIPP), casts the problem as an optimization problem and as such has been proved to be NP-hard. We have designed and developed a new preprocessing procedure for this problem. Our proposed algorithm works with groups of haplotypes rather than individual haplotypes. It iterates searching and deleting haplotypes that are not helpful in order to find the optimal solution. This preprocess can be coupled with any of the current solvers for the HIPP that need to preprocess the genotype data. In order to test it, we have used two state-of-the-art solvers, RTIP and GAHAP, and simulated and real HapMap data. Due to the computational time and memory reduction caused by our preprocess, problem instances that were previously unaffordable can be now efficiently solved.},\n bibtype = {article},\n author = {Irurozki, Ekhine and Calvo, Borja and Lozano, Jose A},\n journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics},\n number = {5}\n}
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\n Haplotype data is especially important in the study of complex diseases since it contains more information than genotype data. However, obtaining haplotype data is technically difficult and expensive. Computational methods have proved to be an effective way of inferring haplotype data from genotype data. One of these methods, the haplotype inference by pure parsimony approach (HIPP), casts the problem as an optimization problem and as such has been proved to be NP-hard. We have designed and developed a new preprocessing procedure for this problem. Our proposed algorithm works with groups of haplotypes rather than individual haplotypes. It iterates searching and deleting haplotypes that are not helpful in order to find the optimal solution. This preprocess can be coupled with any of the current solvers for the HIPP that need to preprocess the genotype data. In order to test it, we have used two state-of-the-art solvers, RTIP and GAHAP, and simulated and real HapMap data. Due to the computational time and memory reduction caused by our preprocess, problem instances that were previously unaffordable can be now efficiently solved.\n
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\n \n\n \n \n \n \n \n A study on the complexity of TSP instances under the 2-exchange neighbor system.\n \n \n \n\n\n \n Hernando, L.; Pascual, J., A.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n In IEEE Symposium on Foundations of Computational Intelligence (FOCI 2011), pages 15-21, 2011. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@inproceedings{\n title = {A study on the complexity of TSP instances under the 2-exchange neighbor system},\n type = {inproceedings},\n year = {2011},\n keywords = {isg_ehu},\n pages = {15-21},\n id = {1c0974f2-9e8e-3471-ab65-e81336c4fac2},\n created = {2021-11-12T08:30:13.976Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:13.976Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Hernando, Leticia and Pascual, Jose A and Mendiburu, Alexander and Lozano, Jose A},\n booktitle = {IEEE Symposium on Foundations of Computational Intelligence (FOCI 2011)}\n}
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\n \n\n \n \n \n \n \n Neural Information Processing: 18th International Conference, ICONIP 2011, Shanghai, China, November 13-17, 2011, Proceedings, Part II.\n \n \n \n\n\n \n Ceberio, J.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n of Lecture Notes in Computer Science. pages 461-470. Lu, B.; Zhang, L.; and Kwok, J., editor(s). Springer Berlin Heidelberg, 2011.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@inbook{\n type = {inbook},\n year = {2011},\n keywords = {isg_ehu},\n pages = {461-470},\n publisher = {Springer Berlin Heidelberg},\n series = {Lecture Notes in Computer Science},\n chapter = {Neural Information Processing: 18th International Conference, ICONIP 2011, Shanghai, China, November 13-17, 2011, Proceedings, Part II},\n id = {c619a159-01bf-3178-b620-92c3dac1fae2},\n created = {2021-11-12T08:30:15.115Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:15.115Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {incollection},\n private_publication = {false},\n bibtype = {inbook},\n author = {Ceberio, Josu and Mendiburu, Alexander and Lozano, Jose A},\n editor = {Lu, Bao-Liang and Zhang, Liqing and Kwok, James}\n}
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\n \n\n \n \n \n \n \n An ensemble of classifiers approach with multiple sources of information.\n \n \n \n\n\n \n Santana, R.; Bielza, C.; and Larrañaga, P.\n\n\n \n\n\n\n In Klami, A., editor(s), Proceedings of ICANN/PASCAL2 Challenge: MEG Mind Reading, of Aalto University Publication series SCIENCE + TECHNOLOGY, pages 25-30, 2011. Aalto University\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {An ensemble of classifiers approach with multiple sources of information},\n type = {inproceedings},\n year = {2011},\n keywords = {isg_ehu},\n pages = {25-30},\n publisher = {Aalto University},\n series = {Aalto University Publication series SCIENCE + TECHNOLOGY},\n id = {01b2552f-ffe0-35fe-a66d-5443f14d5899},\n created = {2021-11-12T08:30:20.662Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:20.662Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {This paper describes the main characteristics of our approach to the ICANN-2011 Mind reading from MEG - PASCAL Challenge. The distinguished features of our method are: 1) The use of different sources of information as input to the classifiers. We simultaneously use information coming from raw data, channels correlations, mutual information between channels, and channel interactions graphs as features for the classifiers. 2) The use of ensemble of classifiers based on regularized multi-logistic regression, regression trees, and an affinity propagation based classifier.},\n bibtype = {inproceedings},\n author = {Santana, R and Bielza, C and Larrañaga, P},\n editor = {Klami, A},\n booktitle = {Proceedings of ICANN/PASCAL2 Challenge: MEG Mind Reading}\n}
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\n This paper describes the main characteristics of our approach to the ICANN-2011 Mind reading from MEG - PASCAL Challenge. The distinguished features of our method are: 1) The use of different sources of information as input to the classifiers. We simultaneously use information coming from raw data, channels correlations, mutual information between channels, and channel interactions graphs as features for the classifiers. 2) The use of ensemble of classifiers based on regularized multi-logistic regression, regression trees, and an affinity propagation based classifier.\n
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\n \n\n \n \n \n \n \n The potential use of a Gadget model to predict stock responses to climate change in combination with Bayesian Networks: the case of the Bay of Biscay anchovy.\n \n \n \n\n\n \n Andonegi, E.; Fernandes, J., A.; Quincoces, I.; Uriarte, A.; Pérez, A.; Howell, D.; and Stefansson, G.\n\n\n \n\n\n\n ICES Journal of Marine Science, 68(6): 1257-1269. 2011.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {The potential use of a Gadget model to predict stock responses to climate change in combination with Bayesian Networks: the case of the Bay of Biscay anchovy},\n type = {article},\n year = {2011},\n keywords = {isg_ehu,isg_jcr},\n pages = {1257-1269},\n volume = {68},\n id = {5acafdc4-8075-38e7-bea2-8299fc2b197f},\n created = {2021-11-12T08:30:30.194Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:30.194Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Andonegi, Eider and Fernandes, Jose A and Quincoces, Iñaki and Uriarte, Andrés and Pérez, Aritz and Howell, Daniel and Stefansson, Guntar},\n journal = {ICES Journal of Marine Science},\n number = {6}\n}
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\n \n\n \n \n \n \n \n Peakbin selection in mass spectrometry data using a consensus approach with estimation of distribution algorithms.\n \n \n \n\n\n \n Armañanzas, R.; Saeys, Y.; Inza, I.; Garc\\'\\ia-Torres, M.; Bielza, C.; de Peer, Y.; and Larrañaga, P.\n\n\n \n\n\n\n IEEE/ACM Transactions on Computational Biology and Bioinformatics, 8(3): 760-774. 2011.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Peakbin selection in mass spectrometry data using a consensus approach with estimation of distribution algorithms},\n type = {article},\n year = {2011},\n keywords = {isg_ehu,isg_jcr},\n pages = {760-774},\n volume = {8},\n id = {e6a69cbf-bec1-3d62-a433-444b1bed44cd},\n created = {2021-11-12T08:30:38.628Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:38.628Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Armañanzas, Rubén and Saeys, Yvan and Inza, Iñaki and Garc\\'\\ia-Torres, Miguel and Bielza, Concha and de Peer, Yves and Larrañaga, Pedro},\n journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics},\n number = {3}\n}
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\n \n\n \n \n \n \n \n Quantitative genetics in multi-objective optimization algorithms: From useful insights to effective methods.\n \n \n \n\n\n \n Santana, R.; Karshenas, H.; Bielza, C.; and Larrañaga, P.\n\n\n \n\n\n\n In Proceedings of the 2011 Genetic and Evolutionary Computation Conference GECCO-2011, pages 91-92, 2011. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@inproceedings{\n title = {Quantitative genetics in multi-objective optimization algorithms: From useful insights to effective methods},\n type = {inproceedings},\n year = {2011},\n keywords = {isg_ehu},\n pages = {91-92},\n id = {d0ff2b1e-685a-338e-93fb-394a99fd34e4},\n created = {2021-11-12T08:30:40.366Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:40.366Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {This paper shows that statistical algorithms proposed for the quantitative trait loci (QTL) mapping problem, and the equation of the multivariate response to selection can be of application in multi-objective optimization. We introduce the conditional dominance relationships between the objectives and propose the use of results from QTL analysis and G-matrix theory to the analysis of multi-objective evolutionary algorithms (MOEAs).},\n bibtype = {inproceedings},\n author = {Santana, R and Karshenas, H and Bielza, C and Larrañaga, P},\n booktitle = {Proceedings of the 2011 Genetic and Evolutionary Computation Conference GECCO-2011}\n}
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\n\n\n
\n This paper shows that statistical algorithms proposed for the quantitative trait loci (QTL) mapping problem, and the equation of the multivariate response to selection can be of application in multi-objective optimization. We introduce the conditional dominance relationships between the objectives and propose the use of results from QTL analysis and G-matrix theory to the analysis of multi-objective evolutionary algorithms (MOEAs).\n
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\n \n\n \n \n \n \n \n Data analysis advances in marine science for fisheries management: Supervised classification applications.\n \n \n \n\n\n \n Fernandes Salvador, J., A.\n\n\n \n\n\n\n Ph.D. Thesis, 2011.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@phdthesis{\n title = {Data analysis advances in marine science for fisheries management: Supervised classification applications},\n type = {phdthesis},\n year = {2011},\n institution = {University of the Basque Country},\n id = {caad7222-0bf1-3312-abd2-4199ca4cbb95},\n created = {2021-11-12T08:30:40.928Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:40.928Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {phdthesis},\n private_publication = {false},\n bibtype = {phdthesis},\n author = {Fernandes Salvador, Jose Antonio}\n}
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\n \n\n \n \n \n \n \n Simulating and evaluating interconnection networks with INSEE.\n \n \n \n\n\n \n Navaridas, J.; Miguel-Alonso, J.; Pascual, J., A.; and Ridruejo, F., J.\n\n\n \n\n\n\n Simulation Modelling Practice and Theory, 19(1): 494-515. 2011.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Simulating and evaluating interconnection networks with INSEE},\n type = {article},\n year = {2011},\n keywords = {isg_ehu,isg_jcr},\n pages = {494-515},\n volume = {19},\n id = {cbdb5f29-b8b4-318e-a5ce-98836afc8514},\n created = {2021-11-12T08:30:48.348Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:48.348Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Navaridas, Javier and Miguel-Alonso, José and Pascual, Jose A and Ridruejo, Francisco J},\n journal = {Simulation Modelling Practice and Theory},\n number = {1}\n}
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\n \n\n \n \n \n \n \n Indirect cube: A power-efficient topology for compute clusters.\n \n \n \n\n\n \n Navaridas, J.; and Miguel-Alonso, J.\n\n\n \n\n\n\n Optical Switching and Networking, 8(3): 162-170. 2011.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Indirect cube: A power-efficient topology for compute clusters},\n type = {article},\n year = {2011},\n keywords = {isg_ehu,isg_jcr},\n pages = {162-170},\n volume = {8},\n id = {a69c6ddf-e21c-3df5-a1a4-ac0f35321eb6},\n created = {2021-11-12T08:30:48.925Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:48.925Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Navaridas, Javier and Miguel-Alonso, José},\n journal = {Optical Switching and Networking},\n number = {3}\n}
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\n \n\n \n \n \n \n \n A direct optimization approach to the P300 speller.\n \n \n \n\n\n \n Santana, R.; Muelas, S.; Latorre, A.; and Peña, J., M.\n\n\n \n\n\n\n In Proceedings of the 2011 Genetic and Evolutionary Computation Conference GECCO-2011, pages 1747-1754, 2011. \n \n\n\n\n
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@inproceedings{\n title = {A direct optimization approach to the P300 speller},\n type = {inproceedings},\n year = {2011},\n keywords = {isg_ehu},\n pages = {1747-1754},\n id = {f26952cc-4afc-37e0-a058-86b62c6e08c1},\n created = {2021-11-12T08:30:56.942Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:56.942Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {The P300 component of the brain event-related-potential is one of the most used signals in brain computer interfaces (BCIs). One of the required steps for the application of the P300 paradigm is the identification of this component in the presence of stimuli. In this paper we propose a direct optimization approach to the P300 classification problem. A general formulation of the problem is introduced. Different classes of optimization algorithms are applied to solve the problem and the concepts of k-best and k-worst ensembles of solutions are introduced as a way to improve the accuracy of single solutions. The introduced approaches are able to achieve a classification rate over 80 percentage on test data.},\n bibtype = {inproceedings},\n author = {Santana, R and Muelas, S and Latorre, A and Peña, J M},\n booktitle = {Proceedings of the 2011 Genetic and Evolutionary Computation Conference GECCO-2011}\n}
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\n The P300 component of the brain event-related-potential is one of the most used signals in brain computer interfaces (BCIs). One of the required steps for the application of the P300 paradigm is the identification of this component in the presence of stimuli. In this paper we propose a direct optimization approach to the P300 classification problem. A general formulation of the problem is introduced. Different classes of optimization algorithms are applied to solve the problem and the concepts of k-best and k-worst ensembles of solutions are introduced as a way to improve the accuracy of single solutions. The introduced approaches are able to achieve a classification rate over 80 percentage on test data.\n
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\n \n\n \n \n \n \n \n A Preliminary Study on EDAs for Permutation Problems Based on Marginal-based Models.\n \n \n \n\n\n \n Ceberio, J.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n In Proceeding of the 13th annual conference on Genetic and Evolutionary Computation, pages 609-616, 2011. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {A Preliminary Study on EDAs for Permutation Problems Based on Marginal-based Models},\n type = {inproceedings},\n year = {2011},\n keywords = {isg_ehu},\n pages = {609-616},\n id = {758d8d1f-1a13-390b-8741-32605c3b99c9},\n created = {2021-11-12T08:31:04.340Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:04.340Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Ceberio, Josu and Mendiburu, Alexander and Lozano, Jose A},\n booktitle = {Proceeding of the 13th annual conference on Genetic and Evolutionary Computation}\n}
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\n \n\n \n \n \n \n \n Optimization-based mapping framework for parallel applications.\n \n \n \n\n\n \n Pascual, J., A.; Miguel-Alonso, J.; and Lozano, J., A.\n\n\n \n\n\n\n Journal of Parallel and Distributed Computing, 71(10): 1377-1387. 2011.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Optimization-based mapping framework for parallel applications},\n type = {article},\n year = {2011},\n keywords = {isg_ehu,isg_jcr},\n pages = {1377-1387},\n volume = {71},\n id = {ca54ac02-db6a-3316-9440-99798f654fa2},\n created = {2021-11-12T08:31:11.482Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:11.482Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Pascual, Jose A and Miguel-Alonso, José and Lozano, Jose A},\n journal = {Journal of Parallel and Distributed Computing},\n number = {10}\n}
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\n \n\n \n \n \n \n \n Event-driven configuration of a neural network CMP system over an homogeneous interconnect fabric.\n \n \n \n\n\n \n Khan, M., M.; Rast, A.; Navaridas, J.; Jin, X.; Plana, L.; Luján, M.; Temple, S.; Patterson, C.; Richards, D.; Woods, J., V.; Miguel-Alonso, J.; and Furber, S.\n\n\n \n\n\n\n Parallel Computing, 37(8): 392-409. 2011.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Event-driven configuration of a neural network CMP system over an homogeneous interconnect fabric},\n type = {article},\n year = {2011},\n keywords = {isg_ehu,isg_jcr},\n pages = {392-409},\n volume = {37},\n id = {13be5bca-9ca6-33ca-84d6-fb59131b9d47},\n created = {2021-11-12T08:31:13.172Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:13.172Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Khan, Muhamad M and Rast, Alexander and Navaridas, Javier and Jin, Xin and Plana, Luis and Luján, Mikel and Temple, Steve and Patterson, Cameron and Richards, D and Woods, John V and Miguel-Alonso, José and Furber, Stephen},\n journal = {Parallel Computing},\n number = {8}\n}
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\n \n\n \n \n \n \n \n Multi-objective optimization with joint probabilistic modeling of objectives and variables.\n \n \n \n\n\n \n Karshenas, H.; Santana, R.; Bielza, C.; and Larrañaga, P.\n\n\n \n\n\n\n In Evolutionary Multi-Criterion Optimization: Sixth International Conference, EMO 2011, of Lecture Notes in Computer Science, pages 298-312, 2011. Springer Berlin-Heidelberg\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Multi-objective optimization with joint probabilistic modeling of objectives and variables},\n type = {inproceedings},\n year = {2011},\n keywords = {isg_ehu},\n pages = {298-312},\n publisher = {Springer Berlin-Heidelberg},\n series = {Lecture Notes in Computer Science},\n id = {d33e4cf3-d1f3-3b81-99cd-16447e9c62d2},\n created = {2021-11-12T08:31:19.906Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:19.906Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {The objective values information can be incorporated into the evolutionary algorithms based on probabilistic modeling in order to capture the relationships between objectives and variables. This paper investigates the effects of joining the objective and variable information on the performance of an estimation of distribution algorithm for multi-objective optimization. A joint Gaussian Bayesian network of objectives and variables is learnt and then sampled using the information about currently best obtained objective values as evidence. The experimental results obtained on a set of multi-objective functions and in comparison to two other competitive algorithms are presented and discussed.},\n bibtype = {inproceedings},\n author = {Karshenas, H and Santana, R and Bielza, C and Larrañaga, P},\n booktitle = {Evolutionary Multi-Criterion Optimization: Sixth International Conference, EMO 2011}\n}
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\n The objective values information can be incorporated into the evolutionary algorithms based on probabilistic modeling in order to capture the relationships between objectives and variables. This paper investigates the effects of joining the objective and variable information on the performance of an estimation of distribution algorithm for multi-objective optimization. A joint Gaussian Bayesian network of objectives and variables is learnt and then sampled using the information about currently best obtained objective values as evidence. The experimental results obtained on a set of multi-objective functions and in comparison to two other competitive algorithms are presented and discussed.\n
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\n \n\n \n \n \n \n \n Estimation of distribution algorithms: from available implementations to potential developments.\n \n \n \n\n\n \n Santana, R.\n\n\n \n\n\n\n In Companion proceedings of the 2011 Genetic and Evolutionary Computation Conference GECCO-2011, pages 679-686, 2011. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Estimation of distribution algorithms: from available implementations to potential developments},\n type = {inproceedings},\n year = {2011},\n keywords = {isg_ehu},\n pages = {679-686},\n id = {95a61ca5-0a45-3395-be07-e6b480026c0b},\n created = {2021-11-12T08:31:24.632Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:24.632Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {This paper focuses on the analysis of estimation of distribution algorithms (EDAs) software. The important role played by EDAs implementations in the usability and range of applications of these algorithms is considered. A survey of available EDA software is presented, and classifications based on the class of programming languages and design strategies used for their implementations are discussed. The paper also reviews different directions to improve current EDA implementations. A number of lines for further expanding the areas of application for EDAs software are proposed.},\n bibtype = {inproceedings},\n author = {Santana, R},\n booktitle = {Companion proceedings of the 2011 Genetic and Evolutionary Computation Conference GECCO-2011}\n}
\n
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\n This paper focuses on the analysis of estimation of distribution algorithms (EDAs) software. The important role played by EDAs implementations in the usability and range of applications of these algorithms is considered. A survey of available EDA software is presented, and classifications based on the class of programming languages and design strategies used for their implementations are discussed. The paper also reviews different directions to improve current EDA implementations. A number of lines for further expanding the areas of application for EDAs software are proposed.\n
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\n \n\n \n \n \n \n \n Analyzing limits of effectiveness in different implementations of estimation of distribution algorithms networks.\n \n \n \n\n\n \n Echegoyen, C.; Zhang, Q.; Mendiburu, A.; Santana, R.; and Lozano, J., A.\n\n\n \n\n\n\n Technical Report Department of Computer Science and Artificial Intelligence, University of the Basque Country, 2011.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@techreport{\n title = {Analyzing limits of effectiveness in different implementations of estimation of distribution algorithms networks},\n type = {techreport},\n year = {2011},\n keywords = {isg_ehu,isg_jcr},\n issue = {EHU-KZAA},\n institution = {Department of Computer Science and Artificial Intelligence, University of the Basque Country},\n id = {c3e837fd-5961-38e4-86c6-ce6401c334b4},\n created = {2021-11-12T08:31:26.572Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:26.572Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {techreport},\n private_publication = {false},\n bibtype = {techreport},\n author = {Echegoyen, C and Zhang, Q and Mendiburu, A and Santana, R and Lozano, J A}\n}
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\n \n\n \n \n \n \n \n Learning Naive Bayes Models for Multiple-Instance Learning with Label Proportions.\n \n \n \n\n\n \n Hernández-González, J.; and Inza, I.\n\n\n \n\n\n\n In Proceedings of the 14th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2011, La Laguna, Spain, November 7-11, 2011., pages 134-144, 2011. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Learning Naive Bayes Models for Multiple-Instance Learning with Label Proportions},\n type = {inproceedings},\n year = {2011},\n keywords = {isg_ehu},\n pages = {134-144},\n id = {ae75f868-df99-34aa-8b6e-981a0db0f60f},\n created = {2021-11-12T08:31:27.782Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:27.782Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Hernández-González, Jerónimo and Inza, Iñaki},\n booktitle = {Proceedings of the 14th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2011, La Laguna, Spain, November 7-11, 2011.}\n}
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\n \n\n \n \n \n \n \n Regularized k-order Markov models in EDAs.\n \n \n \n\n\n \n Santana, R.; Karshenas, H.; Bielza, C.; and Larrañaga, P.\n\n\n \n\n\n\n In Proceedings of the 2011 Genetic and Evolutionary Computation Conference GECCO-2011, pages 593-600, 2011. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Regularized k-order Markov models in EDAs},\n type = {inproceedings},\n year = {2011},\n keywords = {isg_ehu},\n pages = {593-600},\n id = {7afc70ed-3800-3df9-abee-0a4e591670d0},\n created = {2021-11-12T08:31:32.508Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:32.508Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {k-order Markov models have been introduced to estimation of distribution algorithms (EDAs) to solve a particular class of optimization problems in which each variable depends on its previous k variables in a given, fixed order. In this paper we investigate the use of regularization as a way to approximate k-order Markov models when k is increased. The introduced regularized models are used to balance the complexity and accuracy of the k-order Markov models. We investigate the behavior of the EDAs in several instances of the hydrophobic-polar (HP) protein problem, a simplified protein folding model. Our preliminary results show that EDAs that use regularized approximations of the k-order Markov models offer a good compromise between complexity and efficiency, and could be an appropriate choice when the number of variables is increased.},\n bibtype = {inproceedings},\n author = {Santana, R and Karshenas, H and Bielza, C and Larrañaga, P},\n booktitle = {Proceedings of the 2011 Genetic and Evolutionary Computation Conference GECCO-2011}\n}
\n
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\n k-order Markov models have been introduced to estimation of distribution algorithms (EDAs) to solve a particular class of optimization problems in which each variable depends on its previous k variables in a given, fixed order. In this paper we investigate the use of regularization as a way to approximate k-order Markov models when k is increased. The introduced regularized models are used to balance the complexity and accuracy of the k-order Markov models. We investigate the behavior of the EDAs in several instances of the hydrophobic-polar (HP) protein problem, a simplified protein folding model. Our preliminary results show that EDAs that use regularized approximations of the k-order Markov models offer a good compromise between complexity and efficiency, and could be an appropriate choice when the number of variables is increased.\n
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\n \n\n \n \n \n \n \n A differential evolution algorithm for the detection of synaptic vesicles.\n \n \n \n\n\n \n LaTorre, A.; Muelas, S.; Peña, J., M.; Santana, R.; Merchan-Perez, A.; and Rodriguez, J., R.\n\n\n \n\n\n\n In Evolutionary Computation (CEC), 2011 IEEE Congress on, pages 1687-1694, 2011. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {A differential evolution algorithm for the detection of synaptic vesicles},\n type = {inproceedings},\n year = {2011},\n keywords = {isg_ehu},\n pages = {1687-1694},\n id = {d4c8d7c6-deb3-3795-ba05-652215cafdb3},\n created = {2021-11-12T08:31:35.906Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:35.906Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {Neurotransmitters used by chemical synapses are stored in synaptic vesicles that accumulate in axon terminals. The number and position of these vesicles have been related to some important functional properties of the synapse. For this reason, an accurate mechanism for semi-automatically counting these small cellular structures will be of great help for neuroscientists. In this paper, we present a Differential Evolution algorithm that quantifies the number of synaptic vesicles in electron micrographs. The algorithm has been tested on several images that have been obtained from the somatosensory cortex of the rat and compared with some traditional approaches for detecting circular structures. Finally, the results have been validated by two independent expert anatomists.},\n bibtype = {inproceedings},\n author = {LaTorre, A and Muelas, S and Peña, J M and Santana, R and Merchan-Perez, A and Rodriguez, J R},\n booktitle = {Evolutionary Computation (CEC), 2011 IEEE Congress on}\n}
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\n Neurotransmitters used by chemical synapses are stored in synaptic vesicles that accumulate in axon terminals. The number and position of these vesicles have been related to some important functional properties of the synapse. For this reason, an accurate mechanism for semi-automatically counting these small cellular structures will be of great help for neuroscientists. In this paper, we present a Differential Evolution algorithm that quantifies the number of synaptic vesicles in electron micrographs. The algorithm has been tested on several images that have been obtained from the somatosensory cortex of the rat and compared with some traditional approaches for detecting circular structures. Finally, the results have been validated by two independent expert anatomists.\n
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\n \n\n \n \n \n \n \n Optimizing brain networks topologies using multi-objective evolutionary computation.\n \n \n \n\n\n \n Santana, R.; Bielza, C.; and Larrañaga, P.\n\n\n \n\n\n\n Neuroinformatics, 9(1): 3-19. 2011.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Optimizing brain networks topologies using multi-objective evolutionary computation},\n type = {article},\n year = {2011},\n keywords = {isg_ehu,isg_jcr},\n pages = {3-19},\n volume = {9},\n id = {a92ecdcf-aece-3da3-9213-c59aca304fec},\n created = {2021-11-12T08:31:38.141Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:38.141Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {The analysis of brain network topological features has served to better understand these networks and reveal particular characteristics of their functional behavior. The distribution of brain network motifs is particularly useful for detecting and describing differences between brain networks and random and computationally optimized artificial networks. In this paper we use a multi-objective evolutionary optimization approach to generate optimized artificial networks that have a number of topological features resembling brain networks. The Pareto set approximation of the optimized networks is used to extract network descriptors that are compared to brain and random network descriptors. To analyze the networks, the clustering coefficient, the average path length, the modularity and the betweenness centrality are computed. We argue that the topological complexity of a brain network can be estimated using the number of evaluations needed by an optim ization algorithm to output artificial networks of similar complexity. For the analyzed network examples, our results indicate that while original brain networks have a reduced structural motif number and a high functional motif number, they are not optimal with respect to these two topological features. We also investigate the correlation between the structural and functional motif numbers, the average path length and the clustering coefficient in random, optimized and brain networks.},\n bibtype = {article},\n author = {Santana, R and Bielza, C and Larrañaga, P},\n journal = {Neuroinformatics},\n number = {1}\n}
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\n The analysis of brain network topological features has served to better understand these networks and reveal particular characteristics of their functional behavior. The distribution of brain network motifs is particularly useful for detecting and describing differences between brain networks and random and computationally optimized artificial networks. In this paper we use a multi-objective evolutionary optimization approach to generate optimized artificial networks that have a number of topological features resembling brain networks. The Pareto set approximation of the optimized networks is used to extract network descriptors that are compared to brain and random network descriptors. To analyze the networks, the clustering coefficient, the average path length, the modularity and the betweenness centrality are computed. We argue that the topological complexity of a brain network can be estimated using the number of evaluations needed by an optim ization algorithm to output artificial networks of similar complexity. For the analyzed network examples, our results indicate that while original brain networks have a reduced structural motif number and a high functional motif number, they are not optimal with respect to these two topological features. We also investigate the correlation between the structural and functional motif numbers, the average path length and the clustering coefficient in random, optimized and brain networks.\n
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\n \n\n \n \n \n \n \n Univariate marginal distribution algorithm dynamics for a class of parametric functions with unitation constraints.\n \n \n \n\n\n \n Lozada-Chang, L.; and Santana, R.\n\n\n \n\n\n\n Information Sciences, 181(11): 2340-2355. 2011.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Univariate marginal distribution algorithm dynamics for a class of parametric functions with unitation constraints},\n type = {article},\n year = {2011},\n keywords = {isg_ehu,isg_jcr},\n pages = {2340-2355},\n volume = {181},\n id = {bdfb315e-40f2-35bf-8406-fdc583490fbd},\n created = {2021-11-12T08:31:38.747Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:38.747Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {In this paper, we introduce a mathematical model for analyzing the dynamics of the univariate marginal distribution algorithm (UMDA) for a class of parametric functions with isolated global optima. We prove a number of results that are used to model the evolution of UMDA probability distributions for this class of functions. We show that a theoretical analysis can assess the effect of the function parameters on the convergence and rate of convergence of UMDA. We also introduce for the first time a long string limit analysis of UMDA. Finally, we relate the results to ongoing research on the application of the estimation of distribution algorithms for problems with unitation constraints.},\n bibtype = {article},\n author = {Lozada-Chang, L and Santana, R},\n journal = {Information Sciences},\n number = {11}\n}
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\n In this paper, we introduce a mathematical model for analyzing the dynamics of the univariate marginal distribution algorithm (UMDA) for a class of parametric functions with isolated global optima. We prove a number of results that are used to model the evolution of UMDA probability distributions for this class of functions. We show that a theoretical analysis can assess the effect of the function parameters on the convergence and rate of convergence of UMDA. We also introduce for the first time a long string limit analysis of UMDA. Finally, we relate the results to ongoing research on the application of the estimation of distribution algorithms for problems with unitation constraints.\n
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\n \n\n \n \n \n \n \n Affinity propagation enhanced by estimation of distribution algorithms.\n \n \n \n\n\n \n Santana, R.; Bielza, C.; and Larrañaga, P.\n\n\n \n\n\n\n In Proceedings of the 2011 Genetic and Evolutionary Computation Conference GECCO-2011, pages 331-338, 2011. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Affinity propagation enhanced by estimation of distribution algorithms},\n type = {inproceedings},\n year = {2011},\n keywords = {isg_ehu},\n pages = {331-338},\n id = {01bf1ee4-c3b3-3737-8b14-87d57e313034},\n created = {2021-11-12T08:31:42.057Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:42.057Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {Tumor classification based on gene expression data can be applied to set appropriate medical treatment according to the specific tumor characteristics. In this paper we propose the use of estimation of distribution algorithms (EDAs) to enhance the performance of affinity propagation (AP) in classification problems. AP is an efficient clustering algorithm based on message-passing methods and which automatically identifies exemplars of each cluster. We introduce an EDA-based procedure to compute the preferences used by the AP algorithm. Our results show that AP performance can be notably improved by using the introduced approach. Furthermore, we present evidence that classification of new data is improved by employing previously identified exemplars with only minor decrease in classification accuracy.},\n bibtype = {inproceedings},\n author = {Santana, R and Bielza, C and Larrañaga, P},\n booktitle = {Proceedings of the 2011 Genetic and Evolutionary Computation Conference GECCO-2011}\n}
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\n Tumor classification based on gene expression data can be applied to set appropriate medical treatment according to the specific tumor characteristics. In this paper we propose the use of estimation of distribution algorithms (EDAs) to enhance the performance of affinity propagation (AP) in classification problems. AP is an efficient clustering algorithm based on message-passing methods and which automatically identifies exemplars of each cluster. We introduce an EDA-based procedure to compute the preferences used by the AP algorithm. Our results show that AP performance can be notably improved by using the introduced approach. Furthermore, we present evidence that classification of new data is improved by employing previously identified exemplars with only minor decrease in classification accuracy.\n
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\n \n\n \n \n \n \n \n Regularized Model Learning in Estimation of Distribution Algorithms for Continuous Optimization Problems.\n \n \n \n\n\n \n Karshenas, H.; Santana, R.; Bielza, C.; and Larrañaga, P.\n\n\n \n\n\n\n Technical Report Department of Artificial Intelligence, Faculty of Informatics, Technical University of Madrid, 2011.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@techreport{\n title = {Regularized Model Learning in Estimation of Distribution Algorithms for Continuous Optimization Problems},\n type = {techreport},\n year = {2011},\n keywords = {isg_ehu,isg_jcr},\n issue = {UPM-FI/DIA/2011-1},\n institution = {Department of Artificial Intelligence, Faculty of Informatics, Technical University of Madrid},\n id = {4416335e-de27-3697-9657-30abd25e72de},\n created = {2021-11-12T08:31:45.834Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:45.834Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {techreport},\n private_publication = {false},\n bibtype = {techreport},\n author = {Karshenas, H and Santana, R and Bielza, C and Larrañaga, P}\n}
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\n \n\n \n \n \n \n \n On the limits of effectiveness in estimation of distribution algorithms.\n \n \n \n\n\n \n Echegoyen, C.; Zhang, Q.; Mendiburu, A.; Santana, R.; and Lozano, J., A.\n\n\n \n\n\n\n In Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2011, New Orleans, LA, USA, 5-8 June, 2011, pages 1573-1580, 2011. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {On the limits of effectiveness in estimation of distribution algorithms},\n type = {inproceedings},\n year = {2011},\n keywords = {isg_ehu},\n pages = {1573-1580},\n id = {fc168709-3873-311e-85aa-ea2f5c124fb4},\n created = {2021-11-12T08:31:54.165Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:54.165Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Echegoyen, Carlos and Zhang, Qingfu and Mendiburu, Alexander and Santana, Roberto and Lozano, José A},\n booktitle = {Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2011, New Orleans, LA, USA, 5-8 June, 2011}\n}
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\n \n\n \n \n \n \n \n Increasing power of genome-wide association studies by collecting additional single-nucleotide polymorphisms.\n \n \n \n\n\n \n Kostem, E.; Lozano, J., A.; and Eskin, E.\n\n\n \n\n\n\n Genetics, 188(2): 449-460. 2011.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Increasing power of genome-wide association studies by collecting additional single-nucleotide polymorphisms.},\n type = {article},\n year = {2011},\n keywords = {isg_ehu,isg_jcr},\n pages = {449-460},\n volume = {188},\n id = {cf5ef5a4-a86b-33f3-8aa2-6b2090267c38},\n created = {2021-11-12T08:32:02.335Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:02.335Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Kostem, Emrah and Lozano, Jose A and Eskin, Eleazar},\n journal = {Genetics},\n number = {2}\n}
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\n  \n 2010\n \n \n (19)\n \n \n
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\n \n\n \n \n \n \n \n Supervised classification in continuous domains with Bayesian networks.\n \n \n \n\n\n \n Pérez Martínez, A.\n\n\n \n\n\n\n Ph.D. Thesis, 2010.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@phdthesis{\n title = {Supervised classification in continuous domains with Bayesian networks},\n type = {phdthesis},\n year = {2010},\n institution = {University of the Basque Country},\n id = {56a96824-82f7-35c2-9ceb-f7b6979bd15e},\n created = {2021-11-12T08:30:05.350Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:05.350Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {phdthesis},\n private_publication = {false},\n bibtype = {phdthesis},\n author = {Pérez Martínez, Aritz}\n}
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\n \n\n \n \n \n \n \n Porting Estimation of Distribution Algorithms to the Cell Broadband Engine.\n \n \n \n\n\n \n Pérez-Miguel, C.; Miguel-Alonso, J.; and Mendiburu, A.\n\n\n \n\n\n\n Parallel Computing, 36(10-11): 618-634. 5 2010.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Porting Estimation of Distribution Algorithms to the Cell Broadband Engine},\n type = {article},\n year = {2010},\n pages = {618-634},\n volume = {36},\n month = {5},\n id = {9b9ae2dd-ec07-36c1-94b8-3eb451978ae0},\n created = {2021-11-12T08:30:05.609Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:05.609Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Pérez-Miguel, Carlos and Miguel-Alonso, Jose and Mendiburu, Alexander},\n journal = {Parallel Computing},\n number = {10-11}\n}
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\n \n\n \n \n \n \n \n Fish recruitment prediction, using robust supervised classification methods.\n \n \n \n\n\n \n Fernandes, J., A.; Irigoien, X.; Goikoetxea, N.; Lozano, J., A.; Inza, I.; Pérez, A.; and Bode, A.\n\n\n \n\n\n\n Ecological Modelling, 221(2). 2010.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Fish recruitment prediction, using robust supervised classification methods},\n type = {article},\n year = {2010},\n keywords = {isg_ehu,isg_jcr},\n volume = {221},\n id = {8944aa7f-3e90-3cc9-acca-5845f4ac4589},\n created = {2021-11-12T08:30:09.157Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:09.157Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Fernandes, Jose A and Irigoien, Xabier and Goikoetxea, Nerea and Lozano, Jose A and Inza, Iñaki and Pérez, Aritz and Bode, Antonio},\n journal = {Ecological Modelling},\n number = {2}\n}
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\n \n\n \n \n \n \n \n Evolutionary Learning and Optimization.\n \n \n \n\n\n \n Echegoyen, C.; Mendiburu, A.; Santana, R.; and Lozano, J., A.\n\n\n \n\n\n\n Springer, 2010.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inbook{\n type = {inbook},\n year = {2010},\n publisher = {Springer},\n chapter = {Evolutionary Learning and Optimization},\n id = {ce82e898-1927-3de8-8029-dbedd86f0690},\n created = {2021-11-12T08:30:14.852Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:14.852Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {incollection},\n private_publication = {false},\n bibtype = {inbook},\n author = {Echegoyen, C and Mendiburu, A and Santana, R and Lozano, J A},\n editor = {undefined Y.-P. Chen, undefined}\n}
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\n \n\n \n \n \n \n \n A Semi-supervised Approach to Multi-dimensional Classification with Application to Sentiment Analysis.\n \n \n \n\n\n \n Ortigosa-Hernández, J.; Rodríguez, J., D.; Alzate, L.; Inza, I.; and Lozano, J., A.\n\n\n \n\n\n\n In Proceedings of the V Simposio de Teoria y Aplicaciones de Mineria de Datos (TAMIDA 2010), pages 129-138, 2010. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {A Semi-supervised Approach to Multi-dimensional Classification with Application to Sentiment Analysis},\n type = {inproceedings},\n year = {2010},\n keywords = {isg_ehu},\n pages = {129-138},\n id = {6a88deba-97f1-335e-96ff-714d78419e1e},\n created = {2021-11-12T08:30:16.268Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:16.268Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Ortigosa-Hernández, J and Rodríguez, J D and Alzate, L and Inza, I and Lozano, J A},\n booktitle = {Proceedings of the V Simposio de Teoria y Aplicaciones de Mineria de Datos (TAMIDA 2010)}\n}
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\n \n\n \n \n \n \n \n Machine learning: an indispensable tool in bioinformatics.\n \n \n \n\n\n \n Inza, I.; Calvo, B.; Armañanzas, R.; Bengoetxea, E.; Larrañaga, P.; and Lozano, J., A.\n\n\n \n\n\n\n Methods in molecular biology (Clifton, N.J.), 593: 25-48. 2010.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Machine learning: an indispensable tool in bioinformatics.},\n type = {article},\n year = {2010},\n pages = {25-48},\n volume = {593},\n id = {cfaf5424-8712-3baf-9703-32fa177c7b27},\n created = {2021-11-12T08:30:21.486Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:21.486Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {The increase in the number and complexity of biological databases has raised the need for modern and powerful data analysis tools and techniques. In order to fulfill these requirements, the machine learning discipline has become an everyday tool in bio-laboratories. The use of machine learning techniques has been extended to a wide spectrum of bioinformatics applications. It is broadly used to investigate the underlying mechanisms and interactions between biological molecules in many diseases, and it is an essential tool in any biomarker discovery process. In this chapter, we provide a basic taxonomy of machine learning algorithms, and the characteristics of main data preprocessing, supervised classification, and clustering techniques are shown. Feature selection, classifier evaluation, and two supervised classification topics that have a deep impact on current bioinformatics are presented. We make the interested reader aware of a set of popular web resources, open source software tools, and benchmarking data repositories that are frequently used by the machine learning community.},\n bibtype = {article},\n author = {Inza, I and Calvo, B and Armañanzas, R and Bengoetxea, E and Larrañaga, P and Lozano, J A},\n journal = {Methods in molecular biology (Clifton, N.J.)}\n}
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\n The increase in the number and complexity of biological databases has raised the need for modern and powerful data analysis tools and techniques. In order to fulfill these requirements, the machine learning discipline has become an everyday tool in bio-laboratories. The use of machine learning techniques has been extended to a wide spectrum of bioinformatics applications. It is broadly used to investigate the underlying mechanisms and interactions between biological molecules in many diseases, and it is an essential tool in any biomarker discovery process. In this chapter, we provide a basic taxonomy of machine learning algorithms, and the characteristics of main data preprocessing, supervised classification, and clustering techniques are shown. Feature selection, classifier evaluation, and two supervised classification topics that have a deep impact on current bioinformatics are presented. We make the interested reader aware of a set of popular web resources, open source software tools, and benchmarking data repositories that are frequently used by the machine learning community.\n
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\n \n\n \n \n \n \n \n Synergies between network-based representations and probabilistic graphical modeling in the solution of problems from neuroscience.\n \n \n \n\n\n \n Santana, R.; Bielza, C.; and Larrañaga, P.\n\n\n \n\n\n\n In undefined N, editor(s), Proceedings of the Twenty Third International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, volume 6098, of Lecture Notes in Artificial Intelligence, pages 149-158, 2010. Springer\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Synergies between network-based representations and probabilistic graphical modeling in the solution of problems from neuroscience},\n type = {inproceedings},\n year = {2010},\n keywords = {isg_ehu},\n pages = {149-158},\n volume = {6098},\n publisher = {Springer},\n series = {Lecture Notes in Artificial Intelligence},\n id = {b64f19e5-9932-3880-944c-223a4fbfd3af},\n created = {2021-11-12T08:30:34.515Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:34.515Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {Neural systems network-based representations are useful tools to analyze numerous phenomena in neuroscience. Probabilistic graphical models (PGMs) give a concise and still rich representation of complex systems from different domains, including neural systems. In this paper we analyze the characteristics of a bidirectional relationship between networks-based representations and PGMs. We show the way in which this relationship can be exploited introducing a number of methods for the solution of classification, inference and optimization problems. To illustrate the applicability of the introduced methods, a number of problems from the field of neuroscience, in which ongoing research is conducted, are used.},\n bibtype = {inproceedings},\n author = {Santana, R and Bielza, Concha and Larrañaga, Pedro},\n editor = {undefined N, undefined},\n booktitle = {Proceedings of the Twenty Third International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems}\n}
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\n Neural systems network-based representations are useful tools to analyze numerous phenomena in neuroscience. Probabilistic graphical models (PGMs) give a concise and still rich representation of complex systems from different domains, including neural systems. In this paper we analyze the characteristics of a bidirectional relationship between networks-based representations and PGMs. We show the way in which this relationship can be exploited introducing a number of methods for the solution of classification, inference and optimization problems. To illustrate the applicability of the introduced methods, a number of problems from the field of neuroscience, in which ongoing research is conducted, are used.\n
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\n \n\n \n \n \n \n \n Estimation of Bayesian networks algorithms in a class of complex networks.\n \n \n \n\n\n \n Echegoyen, C.; Mendiburu, A.; Santana, R.; and Lozano, J., A.\n\n\n \n\n\n\n In IEEE Congress on Evolutionary Computation, pages 1-8, 5 2010. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Estimation of Bayesian networks algorithms in a class of complex networks},\n type = {inproceedings},\n year = {2010},\n pages = {1-8},\n month = {5},\n id = {650571d6-2fcb-300e-ab3c-aac2d009cb21},\n created = {2021-11-12T08:30:35.892Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:35.892Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {In many optimization problems, regardless of the domain to which it belongs, the structural component that the interactions among variables provides can be seen as a network. The impact that the topological characteristics of that network has, both in the hardness of the problem and in the performance of the optimization techniques, constitutes a very important subject of research. In this paper, we study the behavior of estimation of distribution algorithms (EDAs) in functions whose structure is defined by using different network topologies which include grids, small-world networks and random graphs. In order to do that, we use several descriptors such as the population size, the number of evaluations as well as the structures learned during the search. Furthermore, we take measures from the field of complex networks such as clustering coefficient or characteristic path length in order to quantify the topological properties of the function structure and analyze their relation with the behavior of EDAs. The results show that these measures are useful to have better understanding of this type of algorithms which have exhibited a high sensitivity to the topological characteristics of the function structure. This study creates a link between EDAs based on Bayesian networks and the emergent field of complex networks.},\n bibtype = {inproceedings},\n author = {Echegoyen, Carlos and Mendiburu, Alexander and Santana, Roberto and Lozano, Jose A},\n booktitle = {IEEE Congress on Evolutionary Computation}\n}
\n
\n\n\n
\n In many optimization problems, regardless of the domain to which it belongs, the structural component that the interactions among variables provides can be seen as a network. The impact that the topological characteristics of that network has, both in the hardness of the problem and in the performance of the optimization techniques, constitutes a very important subject of research. In this paper, we study the behavior of estimation of distribution algorithms (EDAs) in functions whose structure is defined by using different network topologies which include grids, small-world networks and random graphs. In order to do that, we use several descriptors such as the population size, the number of evaluations as well as the structures learned during the search. Furthermore, we take measures from the field of complex networks such as clustering coefficient or characteristic path length in order to quantify the topological properties of the function structure and analyze their relation with the behavior of EDAs. The results show that these measures are useful to have better understanding of this type of algorithms which have exhibited a high sensitivity to the topological characteristics of the function structure. This study creates a link between EDAs based on Bayesian networks and the emergent field of complex networks.\n
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\n \n\n \n \n \n \n \n Avances en Algoritmos de Estimación de Distribuciones. Alternativas en el Aprendizaje y Representación de Problemas.\n \n \n \n\n\n \n Miquelez, T.\n\n\n \n\n\n\n Ph.D. Thesis, 2010.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@phdthesis{\n title = {Avances en Algoritmos de Estimación de Distribuciones. Alternativas en el Aprendizaje y Representación de Problemas},\n type = {phdthesis},\n year = {2010},\n institution = {University of the Basque Country},\n id = {81762d73-8fb5-3c83-a31a-8a05841771e6},\n created = {2021-11-12T08:30:43.138Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:43.138Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {phdthesis},\n private_publication = {false},\n bibtype = {phdthesis},\n author = {Miquelez, Teresa}\n}
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\n \n\n \n \n \n \n \n Mateda-2.0: A MATLAB package for the implementation and analysis of estimation of distribution algorithms.\n \n \n \n\n\n \n Santana, R.; Bielza, C.; Larrañaga, P.; Lozano, J., A.; Echegoyen, C.; Mendiburu, A.; Armañanzas, R.; and Shakya, S.\n\n\n \n\n\n\n Journal of Statistical Software, 35(7): 1-30. 2010.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Mateda-2.0: A MATLAB package for the implementation and analysis of estimation of distribution algorithms},\n type = {article},\n year = {2010},\n keywords = {isg_ehu,isg_jcr},\n pages = {1-30},\n volume = {35},\n publisher = {American Statistical Association},\n id = {6d575a41-14e6-3b1b-b7b9-c6410dcb333d},\n created = {2021-11-12T08:30:43.412Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:43.412Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {This paper describes Mateda-2.0, a MATLAB package for estimation of distribution algorithms (EDAs). This package can be used to solve single and multi-objective discrete and continuous optimization problems using EDAs based on undirected and directed probabilistic graphical models. The implementation contains several methods commonly employed by EDAs. It is also conceived as an open package to allow users to incorporate different combinations of selection, learning, sampling, and local search procedures. Additionally, it includes methods to extract, process and visualize the structures learned by the probabilistic models. This way, it can unveil previously unknown information about the optimization problem domain. Mateda-2.0 also incorporates a module for creating and validating function models based on the probabilistic models learned by EDAs.},\n bibtype = {article},\n author = {Santana, R and Bielza, C and Larrañaga, P and Lozano, J A and Echegoyen, C and Mendiburu, A and Armañanzas, R and Shakya, S},\n journal = {Journal of Statistical Software},\n number = {7}\n}
\n
\n\n\n
\n This paper describes Mateda-2.0, a MATLAB package for estimation of distribution algorithms (EDAs). This package can be used to solve single and multi-objective discrete and continuous optimization problems using EDAs based on undirected and directed probabilistic graphical models. The implementation contains several methods commonly employed by EDAs. It is also conceived as an open package to allow users to incorporate different combinations of selection, learning, sampling, and local search procedures. Additionally, it includes methods to extract, process and visualize the structures learned by the probabilistic models. This way, it can unveil previously unknown information about the optimization problem domain. Mateda-2.0 also incorporates a module for creating and validating function models based on the probabilistic models learned by EDAs.\n
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\n \n\n \n \n \n \n \n Generación de matrices para evaluar el desempeño de estrategias de búsqueda de testores t\\'\\ipicos.\n \n \n \n\n\n \n Alba-Cabrera, E.; and Santana, R.\n\n\n \n\n\n\n Avances en Ciencias e Ingenier\\'\\ias, 2: A30–A35. 2010.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Generación de matrices para evaluar el desempeño de estrategias de búsqueda de testores t\\'\\ipicos},\n type = {article},\n year = {2010},\n keywords = {isg_ehu,isg_jcr},\n pages = {A30–A35},\n volume = {2},\n id = {244cefb2-fc67-3cf2-9747-74f9858d3963},\n created = {2021-11-12T08:30:55.556Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:55.556Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {Los testores, y en particular los testores t\\'\\ipicos, han sido utilizados en problemas de selección de variable y problemas de clasificación supervisada. Comunmente se ha usado algoritmos determin\\'\\isticos para hallar testores t\\'\\ipicos. A principios de esta decada comenzó a desarrollarse un nuevo enfoque basado en algoritmos evolutivos. Un problema común para probar el comportamiento de ambos métodos es la necesidad de conocer a priori el número de testores t\\'\\ipicos de una matriz dada. Para una matriz arbitraria, no se puede saber este número a menos de que se hayan encontrado todos los testores t\\'\\ipicos. Por lo tanto, este trabajo introduce, por primera vez, una estrategia para generar matrices básicas para las cuales el número de testores t\\'\\ipicos es conocido sin necesidad de aplicar un algoritmo para encontrarlos. Este método se ilustra con algunos ejemplos.},\n bibtype = {article},\n author = {Alba-Cabrera, Eduardo and Santana, R},\n journal = {Avances en Ciencias e Ingenier\\'\\ias}\n}
\n
\n\n\n
\n Los testores, y en particular los testores t\\'\\ipicos, han sido utilizados en problemas de selección de variable y problemas de clasificación supervisada. Comunmente se ha usado algoritmos determin\\'\\isticos para hallar testores t\\'\\ipicos. A principios de esta decada comenzó a desarrollarse un nuevo enfoque basado en algoritmos evolutivos. Un problema común para probar el comportamiento de ambos métodos es la necesidad de conocer a priori el número de testores t\\'\\ipicos de una matriz dada. Para una matriz arbitraria, no se puede saber este número a menos de que se hayan encontrado todos los testores t\\'\\ipicos. Por lo tanto, este trabajo introduce, por primera vez, una estrategia para generar matrices básicas para las cuales el número de testores t\\'\\ipicos es conocido sin necesidad de aplicar un algoritmo para encontrarlos. Este método se ilustra con algunos ejemplos.\n
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\n \n\n \n \n \n \n \n Network measures for re-using problem information in EDAs.\n \n \n \n\n\n \n Santana, R.; Bielza, C.; and Larrañaga, P.\n\n\n \n\n\n\n Technical Report Department of Artificial Intelligence, Faculty of Informatics, Technical University of Madrid, 2010.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@techreport{\n title = {Network measures for re-using problem information in EDAs},\n type = {techreport},\n year = {2010},\n keywords = {isg_ehu,isg_jcr},\n issue = {UPM-FI/DIA/2010-3},\n institution = {Department of Artificial Intelligence, Faculty of Informatics, Technical University of Madrid},\n id = {4403e585-40bd-33c4-907c-fda34a94452a},\n created = {2021-11-12T08:30:58.223Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:58.223Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {techreport},\n private_publication = {false},\n abstract = {Probabilistic graphical models (PGMs) are used in estimation of distribution algorithms (EDAs) as a model of the search space. Graphical components of PGMs can be also analyzed as networks. In this paper we show that topological measures extracted from these networks capture characteristic information of the optimization problem. The measures can be also used to describe the EDA behavior. Using a simplified protein folding optimization problem, we show that the network information extracted from a set of problem instances can be effectively used to predict characteristics of similar instances.},\n bibtype = {techreport},\n author = {Santana, R and Bielza, C and Larrañaga, P}\n}
\n
\n\n\n
\n Probabilistic graphical models (PGMs) are used in estimation of distribution algorithms (EDAs) as a model of the search space. Graphical components of PGMs can be also analyzed as networks. In this paper we show that topological measures extracted from these networks capture characteristic information of the optimization problem. The measures can be also used to describe the EDA behavior. Using a simplified protein folding optimization problem, we show that the network information extracted from a set of problem instances can be effectively used to predict characteristics of similar instances.\n
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\n \n\n \n \n \n \n \n Using probabilistic dependencies improves the search of conductance-based compartmental neuron models.\n \n \n \n\n\n \n Santana, R.; Bielza, C.; and Larrañaga, P.\n\n\n \n\n\n\n In Pizzuti, C.; Ritchie, M., D.; and Giacobini, M., editor(s), Proceedings of the 8th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, volume 6023, of Lecture Notes in Artificial Intelligence, pages 170-181, 2010. Springer\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Using probabilistic dependencies improves the search of conductance-based compartmental neuron models},\n type = {inproceedings},\n year = {2010},\n keywords = {isg_ehu},\n pages = {170-181},\n volume = {6023},\n publisher = {Springer},\n series = {Lecture Notes in Artificial Intelligence},\n id = {904c17e2-e414-333c-bcc2-5d1f33b9f0f9},\n created = {2021-11-12T08:31:07.626Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:07.626Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {Conductance-based compartmental neuron models are traditionally used to investigate the electrophysiological properties of neurons. These models require a number of parameters to be adjusted to biological experimental data and this question can be posed as an optimization problem. In this paper we investigate the behavior of different estimation of distribution algorithms (EDAs) for this problem. We focus on studying the influence that the interactions between the neuron model conductances have in the complexity of the optimization problem. We support evidence that the use of these interactions during the optimization process can improve the EDA behavior.},\n bibtype = {inproceedings},\n author = {Santana, R and Bielza, C and Larrañaga, P},\n editor = {Pizzuti, Clara and Ritchie, Marylyn D and Giacobini, Mario},\n booktitle = {Proceedings of the 8th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics}\n}
\n
\n\n\n
\n Conductance-based compartmental neuron models are traditionally used to investigate the electrophysiological properties of neurons. These models require a number of parameters to be adjusted to biological experimental data and this question can be posed as an optimization problem. In this paper we investigate the behavior of different estimation of distribution algorithms (EDAs) for this problem. We focus on studying the influence that the interactions between the neuron model conductances have in the complexity of the optimization problem. We support evidence that the use of these interactions during the optimization process can improve the EDA behavior.\n
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\n \n\n \n \n \n \n \n Bivariate empirical and n-variate Archimedean copulas in estimation of distribution algorithms.\n \n \n \n\n\n \n Cuesta-Infante, A.; Santana, R.; Hidalgo, J., I.; Bielza, C.; and Larrañaga, P.\n\n\n \n\n\n\n In Proceedings of the 2010 Congress on Evolutionary Computation CEC-2010, pages 1-8, 2010. IEEE\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Bivariate empirical and n-variate Archimedean copulas in estimation of distribution algorithms},\n type = {inproceedings},\n year = {2010},\n keywords = {isg_ehu},\n pages = {1-8},\n publisher = {IEEE},\n id = {b017697c-4d80-3a20-be23-e8e695497d79},\n created = {2021-11-12T08:31:22.984Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:22.984Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {This paper investigates the use of empirical and Archimedean copulas as probabilistic models of continuous estimation of distribution algorithms (EDAs). A method for learning and sampling empirical bivariate copulas to be used in the context of n-dimensional EDAs is first introduced. Then, by using Archimedean copulas instead of empirical makes possible to construct n-dimensional copulas with the same purpose. Both copula-based EDAs are compared to other known continuous EDAs on a set of 24 functions and different number of variables. Experimental results show that the proposed copula-based EDAs achieve a better behaviour than previous approaches in a 20 percentage of the benchmark functions.},\n bibtype = {inproceedings},\n author = {Cuesta-Infante, Alfredo and Santana, Roberto and Hidalgo, J Ignacio and Bielza, Concha and Larrañaga, Pedro},\n booktitle = {Proceedings of the 2010 Congress on Evolutionary Computation CEC-2010}\n}
\n
\n\n\n
\n This paper investigates the use of empirical and Archimedean copulas as probabilistic models of continuous estimation of distribution algorithms (EDAs). A method for learning and sampling empirical bivariate copulas to be used in the context of n-dimensional EDAs is first introduced. Then, by using Archimedean copulas instead of empirical makes possible to construct n-dimensional copulas with the same purpose. Both copula-based EDAs are compared to other known continuous EDAs on a set of 24 functions and different number of variables. Experimental results show that the proposed copula-based EDAs achieve a better behaviour than previous approaches in a 20 percentage of the benchmark functions.\n
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\n \n\n \n \n \n \n \n Twisted Torus Topologies for Enhanced Interconnection Networks.\n \n \n \n\n\n \n Cámara, J., M.; Moretó, M.; Vallejo, E.; Beivide, R.; Miguel-Alonso, J.; Mart\\'\\inez, C.; and Navaridas, J.\n\n\n \n\n\n\n IEEE Trans. Parallel Distrib. Syst., 21(12): 1765-1778. 2010.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Twisted Torus Topologies for Enhanced Interconnection Networks},\n type = {article},\n year = {2010},\n keywords = {isg_ehu,isg_jcr},\n pages = {1765-1778},\n volume = {21},\n id = {8ec9eaf2-c928-3f63-934d-7fa461cd6a92},\n created = {2021-11-12T08:31:26.844Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:26.844Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Cámara, José M and Moretó, Miquel and Vallejo, Enrique and Beivide, Ramón and Miguel-Alonso, José and Mart\\'\\inez, Carmen and Navaridas, Javier},\n journal = {IEEE Trans. Parallel Distrib. Syst.},\n number = {12}\n}
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\n \n\n \n \n \n \n \n Reducing complexity in tree-like computer interconnection networks.\n \n \n \n\n\n \n Navaridas, J.; Miguel-Alonso, J.; Ridruejo, F., J.; and Denzel, W.\n\n\n \n\n\n\n Parallel Computing, 36(2-3): 71-85. 5 2010.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Reducing complexity in tree-like computer interconnection networks},\n type = {article},\n year = {2010},\n pages = {71-85},\n volume = {36},\n month = {5},\n id = {69a2c912-e2b7-3878-85f1-73aed63dab9f},\n created = {2021-11-12T08:31:31.034Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:31.034Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Navaridas, Javier and Miguel-Alonso, Jose and Ridruejo, Francisco Javier and Denzel, Wolfgang},\n journal = {Parallel Computing},\n number = {2-3}\n}
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\n \n\n \n \n \n \n \n Learning factorizations in estimation of distribution algorithms using affinity propagation.\n \n \n \n\n\n \n Santana, R.; Larrañaga, P.; and Lozano, J., A.\n\n\n \n\n\n\n Evolutionary Computation, 18(4): 515-546. 2010.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Learning factorizations in estimation of distribution algorithms using affinity propagation},\n type = {article},\n year = {2010},\n keywords = {isg_ehu,isg_jcr},\n pages = {515-546},\n volume = {18},\n id = {49bc1a5a-42dd-3e61-af2d-d5bc21629380},\n created = {2021-11-12T08:31:39.018Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:39.018Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {Estimation of distribution algorithms (EDAs) that use marginal product model factorizations have been widely applied to a broad range of mainly binary optimization problems. In this paper, we introduce the affinity propagation EDA (AffEDA) which learns a marginal product model by clustering a matrix of mutual information learned from the data using a very efficient message-passing algorithm known as affinity propagation. The introduced algorithm is tested on a set of binary and nonbinary decomposable functions and using a hard combinatorial class of problem known as the HP protein model. The results show that the algorithm is a very efficient alternative to other EDAs that use marginal product model factorizations such as the extended compact genetic algorithm (ECGA) and improves the quality of the results achieved by ECGA when the cardinality of the variables is increased.},\n bibtype = {article},\n author = {Santana, R and Larrañaga, P and Lozano, J A},\n journal = {Evolutionary Computation},\n number = {4}\n}
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\n Estimation of distribution algorithms (EDAs) that use marginal product model factorizations have been widely applied to a broad range of mainly binary optimization problems. In this paper, we introduce the affinity propagation EDA (AffEDA) which learns a marginal product model by clustering a matrix of mutual information learned from the data using a very efficient message-passing algorithm known as affinity propagation. The introduced algorithm is tested on a set of binary and nonbinary decomposable functions and using a hard combinatorial class of problem known as the HP protein model. The results show that the algorithm is a very efficient alternative to other EDAs that use marginal product model factorizations such as the extended compact genetic algorithm (ECGA) and improves the quality of the results achieved by ECGA when the cardinality of the variables is increased.\n
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\n \n\n \n \n \n \n \n Estudio preliminar sobre la complejidad de las instancias del TSP bajo el sistema de vecinos 2-opt.\n \n \n \n\n\n \n Hernando, L.; Pascual, J., A.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n In VII Congreso Español sobre Metaheuristicas, Algoritmos Evolutivos y Bioinspirados (MAEB 2010), 2010. \n \n\n\n\n
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\n 
@inproceedings{\n title = {Estudio preliminar sobre la complejidad de las instancias del TSP bajo el sistema de vecinos 2-opt},\n type = {inproceedings},\n year = {2010},\n keywords = {isg_ehu},\n id = {bcc02094-1226-3c96-9e2a-79b11346c62c},\n created = {2021-11-12T08:31:39.300Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:39.300Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Hernando, Leticia and Pascual, Jose A and Mendiburu, Alexander and Lozano, Jose A},\n booktitle = {VII Congreso Español sobre Metaheuristicas, Algoritmos Evolutivos y Bioinspirados (MAEB 2010)}\n}
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\n \n\n \n \n \n \n \n Multi-marker tagging single nucleotide polymorphism selection using estimation of distribution algorithms.\n \n \n \n\n\n \n Santana, R.; Mendiburu, A.; Zaitlen, N.; Eskin, E.; and Lozano, J., A.\n\n\n \n\n\n\n Artificial intelligence in medicine, 50(3): 193-201. 5 2010.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Multi-marker tagging single nucleotide polymorphism selection using estimation of distribution algorithms.},\n type = {article},\n year = {2010},\n keywords = {estimation of distribution algorithms,hapmap,multi-marker selection,tagging single nucleotide polymorphism selection},\n pages = {193-201},\n volume = {50},\n month = {5},\n id = {5c8bef06-2138-3ae2-9ea1-0c022c21662f},\n created = {2021-11-12T08:32:02.038Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:02.038Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {OBJECTIVES: This paper presents an optimization algorithm for the automatic selection of a minimal subset of tagging single nucleotide polymorphisms (SNPs). METHODS AND MATERIALS: The determination of the set of minimal tagging SNPs is approached as an optimization problem in which each tagged SNP can be covered by a single tagging SNP or by a pair of tagging SNPs. The problem is solved using an estimation of distribution algorithm (EDA) which takes advantage of the underlying topological structure defined by the SNP correlations to model the problem interactions. The EDA stochastically searches the constrained space of feasible solutions. It is evaluated across HapMap reference panel data sets. RESULTS: The EDA was compared with a SAT solver, able to find the single-marker minimal tagging sets, and with the Tagger program. The percentage of reduction ranged from 10% to 43% in the number of tagging SNPs of the minimal multi-marker tagging set found by the EDA with respect to the other algorithms. CONCLUSIONS: The introduced algorithm is effective for the identification of minimal multi-marker SNP sets, which considerably reduce the dimension of the tagging SNP set in comparison with single-marker sets. Other variants of the SNP problem can be treated following the same approach.},\n bibtype = {article},\n author = {Santana, Roberto and Mendiburu, Alexander and Zaitlen, Noah and Eskin, Eleazar and Lozano, Jose A},\n journal = {Artificial intelligence in medicine},\n number = {3}\n}
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\n OBJECTIVES: This paper presents an optimization algorithm for the automatic selection of a minimal subset of tagging single nucleotide polymorphisms (SNPs). METHODS AND MATERIALS: The determination of the set of minimal tagging SNPs is approached as an optimization problem in which each tagged SNP can be covered by a single tagging SNP or by a pair of tagging SNPs. The problem is solved using an estimation of distribution algorithm (EDA) which takes advantage of the underlying topological structure defined by the SNP correlations to model the problem interactions. The EDA stochastically searches the constrained space of feasible solutions. It is evaluated across HapMap reference panel data sets. RESULTS: The EDA was compared with a SAT solver, able to find the single-marker minimal tagging sets, and with the Tagger program. The percentage of reduction ranged from 10% to 43% in the number of tagging SNPs of the minimal multi-marker tagging set found by the EDA with respect to the other algorithms. CONCLUSIONS: The introduced algorithm is effective for the identification of minimal multi-marker SNP sets, which considerably reduce the dimension of the tagging SNP set in comparison with single-marker sets. Other variants of the SNP problem can be treated following the same approach.\n
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\n  \n 2009\n \n \n (20)\n \n \n
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\n \n\n \n \n \n \n \n Microarray analysis of autoimmune diseases by machine learning procedures.\n \n \n \n\n\n \n Armañanzas, R.; Calvo, B.; Inza, I., N.; López-Hoyos, M.; Mart\\'\\inez-Taboada, V.; Ucar, E.; Bernales, I.; Fullaondo, A.; Larrañaga, P.; Zubiaga, A., M.; Armananzas, R.; Calvo, B.; Inza, I., N.; López-Hoyos, M.; Mart'inez-Taboada, V.; Ucar, E.; Bernales, I.; Fullaondo, A.; Larranaga, P.; and Zubiaga, A., M.\n\n\n \n\n\n\n IEEE Transactions on Information Technology in Biomedicine, 3(13): 341-350. 2009.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Microarray analysis of autoimmune diseases by machine learning procedures},\n type = {article},\n year = {2009},\n keywords = {isg_ehu,isg_jcr},\n pages = {341-350},\n volume = {3},\n publisher = {IEEE},\n id = {e09dc2b9-8f02-30c5-864b-a6819273a764},\n created = {2021-11-12T08:30:02.706Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:02.706Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Armañanzas, Rubén and Calvo, Borja and Inza, Iñaki Naki and López-Hoyos, Marcos and Mart\\'\\inez-Taboada, V\\'\\ictor and Ucar, Eduardo and Bernales, Irantzu and Fullaondo, Asier and Larrañaga, Pedro and Zubiaga, Ana M and Armananzas, Rubén and Calvo, Borja and Inza, Iñaki Naki and López-Hoyos, Marcos and Mart'inez-Taboada, V'ictor and Ucar, Eduardo and Bernales, Irantzu and Fullaondo, Asier and Larranaga, Pedro and Zubiaga, Ana M},\n journal = {IEEE Transactions on Information Technology in Biomedicine},\n number = {13}\n}
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\n \n\n \n \n \n \n \n Mining probabilistic models learned by EDAs in the optimization of multi-objective problems.\n \n \n \n\n\n \n Santana, R.; Bielza, C.; Lozano, J., A.; and Larrañaga, P.\n\n\n \n\n\n\n In Proceedings of the Genetic and Evolutionary Computation Conference GECCO-2009, pages 445-452, 2009. ACM\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Mining probabilistic models learned by EDAs in the optimization of multi-objective problems},\n type = {inproceedings},\n year = {2009},\n keywords = {isg_ehu},\n pages = {445-452},\n publisher = {ACM},\n id = {5670b261-fb43-346d-b843-a097d9dd0e4d},\n created = {2021-11-12T08:30:06.824Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:06.824Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {One of the uses of the probabilistic models learned by estimation of distribution algorithms is to reveal previous unknown information about the problem structure. In this paper we investigate the mapping between the problem structure and the dependencies captured in the probabilistic models learned by EDAs for a set of multi-objective satisfiability problems. We present and discuss the application of different data mining and visualization techniques for processing and visualizing relevant information from the structure of the learned probabilistic models. We show that also in the case of multi-objective optimization problems, some features of the original problem structure can be translated to the probabilistic models and unveiled by using algorithms that mine the model structures.},\n bibtype = {inproceedings},\n author = {Santana, R and Bielza, C and Lozano, J A and Larrañaga, P},\n booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference GECCO-2009}\n}
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\n One of the uses of the probabilistic models learned by estimation of distribution algorithms is to reveal previous unknown information about the problem structure. In this paper we investigate the mapping between the problem structure and the dependencies captured in the probabilistic models learned by EDAs for a set of multi-objective satisfiability problems. We present and discuss the application of different data mining and visualization techniques for processing and visualizing relevant information from the structure of the learned probabilistic models. We show that also in the case of multi-objective optimization problems, some features of the original problem structure can be translated to the probabilistic models and unveiled by using algorithms that mine the model structures.\n
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\n \n\n \n \n \n \n \n Analyzing the probability of the optimum in EDAs based on Bayesian networks.\n \n \n \n\n\n \n Echegoyen, C.; Mendiburu, A.; Santana, R.; and Lozano, J., A.\n\n\n \n\n\n\n In Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2009, Trondheim, Norway, 18-21 May, 2009, pages 1652-1659, 2009. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@inproceedings{\n title = {Analyzing the probability of the optimum in EDAs based on Bayesian networks},\n type = {inproceedings},\n year = {2009},\n keywords = {isg_ehu},\n pages = {1652-1659},\n id = {621cb2c1-3653-3545-abe3-5b11e8dbd204},\n created = {2021-11-12T08:30:09.684Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:09.684Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Echegoyen, Carlos and Mendiburu, Alexander and Santana, Roberto and Lozano, José A},\n booktitle = {Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2009, Trondheim, Norway, 18-21 May, 2009}\n}
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\n \n\n \n \n \n \n \n Research topics on discrete estimation of distribution algorithms.\n \n \n \n\n\n \n Santana, R.; Larrañaga, P.; and Lozano, J., A.\n\n\n \n\n\n\n Memetic Computing, 1(1): 35-54. 2009.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Research topics on discrete estimation of distribution algorithms},\n type = {article},\n year = {2009},\n keywords = {isg_ehu,isg_jcr},\n pages = {35-54},\n volume = {1},\n id = {fac17af0-9d2f-38c3-8bcd-476b0b6a53d8},\n created = {2021-11-12T08:30:10.378Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:10.378Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {In this paper, we identify a number of topics relevant for the improvement and development of discrete estimation of distribution algorithms. Focusing on the role of probability distributions and factorizations in estimation of distribution algorithms, we present a survey of current challenges where further research must provide answers that extend the potential and applicability of these algorithms. In each case we state the research topic and elaborate on the reasons that make it relevant for estimation of distribution algorithms. In some cases current work or possible alternatives for the solution of the problem are discussed.},\n bibtype = {article},\n author = {Santana, R and Larrañaga, P and Lozano, J A},\n journal = {Memetic Computing},\n number = {1}\n}
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\n In this paper, we identify a number of topics relevant for the improvement and development of discrete estimation of distribution algorithms. Focusing on the role of probability distributions and factorizations in estimation of distribution algorithms, we present a survey of current challenges where further research must provide answers that extend the potential and applicability of these algorithms. In each case we state the research topic and elaborate on the reasons that make it relevant for estimation of distribution algorithms. In some cases current work or possible alternatives for the solution of the problem are discussed.\n
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\n \n\n \n \n \n \n \n Optimizing the number of classes in automated zooplankton classification.\n \n \n \n\n\n \n Fernandes, J., A.; Irigoien, X.; Boyra, G.; Lozano, J., A.; and Inza, I.\n\n\n \n\n\n\n Journal of Plankton Research, 31(1): 19-29. 2009.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Optimizing the number of classes in automated zooplankton classification},\n type = {article},\n year = {2009},\n keywords = {isg_ehu,isg_jcr},\n pages = {19-29},\n volume = {31},\n id = {83681301-12f8-36f5-911b-e78380dd3cf6},\n created = {2021-11-12T08:30:18.141Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:18.141Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Fernandes, Jose A and Irigoien, Xabier and Boyra, Guillermo and Lozano, Jose A and Inza, Iñaki},\n journal = {Journal of Plankton Research},\n number = {1}\n}
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\n \n\n \n \n \n \n \n Clasificadores Bayesianos en la Selección Embrionaria en Tratamientos de Reproducción Asistida.\n \n \n \n\n\n \n Morales Vega, D., A.\n\n\n \n\n\n\n Ph.D. Thesis, 2009.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@phdthesis{\n title = {Clasificadores Bayesianos en la Selección Embrionaria en Tratamientos de Reproducción Asistida},\n type = {phdthesis},\n year = {2009},\n institution = {University of the Basque Country},\n id = {47c279af-fc23-38cf-80ff-23329c7f16df},\n created = {2021-11-12T08:30:25.653Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:25.653Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {phdthesis},\n private_publication = {false},\n bibtype = {phdthesis},\n author = {Morales Vega, Dinora A}\n}
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\n \n\n \n \n \n \n \n Evaluating the cell broadband engine as a platform to run estimation of distribution algorithms.\n \n \n \n\n\n \n Pérez-Miguel, C.; Miguel-Alonso, J.; and Mendiburu, A.\n\n\n \n\n\n\n In Genetic and Evolutionary Computation Conference, GECCO 2009, Proceedings, Montreal, Québec, Canada, July 8-12, 2009, Companion Material, pages 2491-2498, 2009. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Evaluating the cell broadband engine as a platform to run estimation of distribution algorithms},\n type = {inproceedings},\n year = {2009},\n keywords = {isg_ehu},\n pages = {2491-2498},\n id = {97b9b805-ce3f-3e4e-b65c-ab3b27e0b323},\n created = {2021-11-12T08:30:26.227Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:26.227Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Pérez-Miguel, Carlos and Miguel-Alonso, José and Mendiburu, Alexander},\n booktitle = {Genetic and Evolutionary Computation Conference, GECCO 2009, Proceedings, Montreal, Québec, Canada, July 8-12, 2009, Companion Material}\n}
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\n \n\n \n \n \n \n \n Estudio de la probabilidad del óptimo en EDAs basados en redes Bayesianas.\n \n \n \n\n\n \n Echegoyen, C.; Santana, R.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n In Proceedings of the X Congreso Español sobre Metaheur\\'\\isticas, Algoritmos Evolutivos y Bioinspirados (MAEB-2009), 2009. Thomson\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Estudio de la probabilidad del óptimo en EDAs basados en redes Bayesianas},\n type = {inproceedings},\n year = {2009},\n keywords = {isg_ehu},\n publisher = {Thomson},\n id = {260fd3dc-d0df-3ac3-b461-04fa23e6b308},\n created = {2021-11-12T08:30:34.229Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:34.229Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Echegoyen, C and Santana, R and Mendiburu, A and Lozano, J A},\n booktitle = {Proceedings of the X Congreso Español sobre Metaheur\\'\\isticas, Algoritmos Evolutivos y Bioinspirados (MAEB-2009)}\n}
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\n \n\n \n \n \n \n \n Bayesian classifiers based on kernel density estimation: Flexible classifiers.\n \n \n \n\n\n \n Pérez, A.; Larrañaga, P.; and Inza, I.\n\n\n \n\n\n\n International Journal of Approximate Reasoning, 50(2): 341-362. 5 2009.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Bayesian classifiers based on kernel density estimation: Flexible classifiers},\n type = {article},\n year = {2009},\n keywords = {bayesian network,flexible naive bayes,kernel density estimation,supervised classification},\n pages = {341-362},\n volume = {50},\n month = {5},\n id = {e172ff5f-db25-39b1-9b05-adf4195e169b},\n created = {2021-11-12T08:30:41.980Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:41.980Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {When learning Bayesian network based classifiers continuous variables are usually handled by discretization, or assumed that they follow a Gaussian distribution. This work introduces the kernel based Bayesian network paradigm for supervised classification. This paradigm is a Bayesian network which estimates the true density of the continuous variables using kernels. Besides, tree-augmented naive Bayes, k-dependence Bayesian classifier and complete graph classifier are adapted to the novel kernel based Bayesian network paradigm. Moreover, the strong consistency properties of the presented classifiers are proved and an estimator of the mutual information based on kernels is presented. The classifiers presented in this work can be seen as the natural extension of the flexible naive Bayes classifier proposed by John and Langley [G.H. John, P. Langley, Estimating continuous distributions in Bayesian classifiers, in: Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence, 1995, pp. 338–345], breaking with its strong independence assumption. Flexible tree-augmented naive Bayes seems to have superior behavior for supervised classification among the flexible classifiers. Besides, flexible classifiers presented have obtained competitive errors compared with the state-of-the-art classifiers.},\n bibtype = {article},\n author = {Pérez, Aritz and Larrañaga, Pedro and Inza, Iñaki},\n journal = {International Journal of Approximate Reasoning},\n number = {2}\n}
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\n When learning Bayesian network based classifiers continuous variables are usually handled by discretization, or assumed that they follow a Gaussian distribution. This work introduces the kernel based Bayesian network paradigm for supervised classification. This paradigm is a Bayesian network which estimates the true density of the continuous variables using kernels. Besides, tree-augmented naive Bayes, k-dependence Bayesian classifier and complete graph classifier are adapted to the novel kernel based Bayesian network paradigm. Moreover, the strong consistency properties of the presented classifiers are proved and an estimator of the mutual information based on kernels is presented. The classifiers presented in this work can be seen as the natural extension of the flexible naive Bayes classifier proposed by John and Langley [G.H. John, P. Langley, Estimating continuous distributions in Bayesian classifiers, in: Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence, 1995, pp. 338–345], breaking with its strong independence assumption. Flexible tree-augmented naive Bayes seems to have superior behavior for supervised classification among the flexible classifiers. Besides, flexible classifiers presented have obtained competitive errors compared with the state-of-the-art classifiers.\n
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\n \n\n \n \n \n \n \n Feature subset selection from positive and unlabelled examples.\n \n \n \n\n\n \n Calvo, B.; Larranaga, P.; and Lozano, J., A.\n\n\n \n\n\n\n Pattern Recognition Letters, 30(11): 1027-1036. 2009.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Feature subset selection from positive and unlabelled examples},\n type = {article},\n year = {2009},\n keywords = {isg_ehu,isg_jcr},\n pages = {1027-1036},\n volume = {30},\n publisher = {North-Holland},\n id = {c7e46003-6328-371d-9675-9c127e0bca03},\n created = {2021-11-12T08:30:47.160Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:47.160Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Calvo, Borja and Larranaga, Pedro and Lozano, Jose A},\n journal = {Pattern Recognition Letters},\n number = {11}\n}
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\n \n\n \n \n \n \n \n A quantitative analysis of estimation of distribution algorithms based on Bayesian networks.\n \n \n \n\n\n \n Echegoyen, C.; Mendiburu, A.; Santana, R.; and Lozano, J., A.\n\n\n \n\n\n\n Technical Report Department of Computer Science and Artificial Intelligence, University of the Basque Country, 2009.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@techreport{\n title = {A quantitative analysis of estimation of distribution algorithms based on Bayesian networks},\n type = {techreport},\n year = {2009},\n keywords = {isg_ehu,isg_jcr},\n issue = {EHU-KZAA-IK-3},\n institution = {Department of Computer Science and Artificial Intelligence, University of the Basque Country},\n id = {099d7d18-7f8e-38b0-b8bf-735f2517ae7b},\n created = {2021-11-12T08:30:52.359Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:52.359Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {techreport},\n private_publication = {false},\n abstract = {The successful application of estimation of distribution algorithms (EDAs) to solve different kinds of problems has reinforced their candidature as promising black-box optimization tools. However, their internal behavior is still not completely understood and therefore it is necessary to work in this direction in order to advance their development. This paper presents a new methodology of analysis which provides new information about the behavior of EDAs by quantitatively analyzing the probabilistic models learned during the search. We particularly focus on calculating the probabilities of the optimal solutions, the most probable solution given by the model and the best individual of the population at each step of the algorithm. We carry out the analysis by optimizing functions of different nature such as Trap5, two variants of Ising spin glass and Max-SAT. By using different structures in the probabilistic models, we also analyze the influence o f the st ructural model accuracy in the quantitative behavior of EDAs. In addition, the objective function values of our analyzed key solutions are contrasted with their probability values in order to study the connection between function and probabilistic models. The results not only show information about the EDA behavior, but also about the quality of the optimization process and setup of the parameters, the relationship between the probabilistic model and the fitness function, and even about the problem itself. Furthermore, the results allow us to discover common patterns of behavior in EDAs and propose new ideas in the development of this type of algorithms.},\n bibtype = {techreport},\n author = {Echegoyen, C and Mendiburu, A and Santana, R and Lozano, J A}\n}
\n
\n\n\n
\n The successful application of estimation of distribution algorithms (EDAs) to solve different kinds of problems has reinforced their candidature as promising black-box optimization tools. However, their internal behavior is still not completely understood and therefore it is necessary to work in this direction in order to advance their development. This paper presents a new methodology of analysis which provides new information about the behavior of EDAs by quantitatively analyzing the probabilistic models learned during the search. We particularly focus on calculating the probabilities of the optimal solutions, the most probable solution given by the model and the best individual of the population at each step of the algorithm. We carry out the analysis by optimizing functions of different nature such as Trap5, two variants of Ising spin glass and Max-SAT. By using different structures in the probabilistic models, we also analyze the influence o f the st ructural model accuracy in the quantitative behavior of EDAs. In addition, the objective function values of our analyzed key solutions are contrasted with their probability values in order to study the connection between function and probabilistic models. The results not only show information about the EDA behavior, but also about the quality of the optimization process and setup of the parameters, the relationship between the probabilistic model and the fitness function, and even about the problem itself. Furthermore, the results allow us to discover common patterns of behavior in EDAs and propose new ideas in the development of this type of algorithms.\n
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\n \n\n \n \n \n \n \n MATEDA: A suite of EDA programs in Matlab.\n \n \n \n\n\n \n Santana, R.; Echegoyen, C.; Mendiburu, A.; Bielza, C.; Lozano, J., A.; Larrañaga, P.; Armañanzas, R.; and Shakya, S.\n\n\n \n\n\n\n Technical Report Department of Computer Science and Artificial Intelligence, University of the Basque Country, 2009.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@techreport{\n title = {MATEDA: A suite of EDA programs in Matlab},\n type = {techreport},\n year = {2009},\n keywords = {isg_ehu,isg_jcr},\n issue = {EHU-KZAA-IK-2/09},\n institution = {Department of Computer Science and Artificial Intelligence, University of the Basque Country},\n id = {b7e77588-00b0-30e5-9f27-4a6e75a8ce87},\n created = {2021-11-12T08:31:06.497Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:06.497Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {techreport},\n private_publication = {false},\n abstract = {This paper describes MATEDA-2.0, a suite of programs in Matlab for estimation of distribution algorithms. The package allows the optimization of single and multi-objective problems with estimation of distribution algorithms (EDAs) based on undirected graphical models and Bayesian networks. The implementation is conceived for allowing the incorporation by the user of different combinations of selection, learning, sampling, and local search procedures. Other included methods allow the analysis of the structures learned by the probabilistic models, the visualization of particular features of these structures and the use of the probabilistic models as fitness modeling tools.},\n bibtype = {techreport},\n author = {Santana, R and Echegoyen, C and Mendiburu, A and Bielza, C and Lozano, J A and Larrañaga, P and Armañanzas, R and Shakya, S}\n}
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\n This paper describes MATEDA-2.0, a suite of programs in Matlab for estimation of distribution algorithms. The package allows the optimization of single and multi-objective problems with estimation of distribution algorithms (EDAs) based on undirected graphical models and Bayesian networks. The implementation is conceived for allowing the incorporation by the user of different combinations of selection, learning, sampling, and local search procedures. Other included methods allow the analysis of the structures learned by the probabilistic models, the visualization of particular features of these structures and the use of the probabilistic models as fitness modeling tools.\n
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\n \n\n \n \n \n \n \n A new preprocessing procedure for the haplotype inference problem.\n \n \n \n\n\n \n Irurozki, E.; and Lozano, J., A.\n\n\n \n\n\n\n In IEEE Congress on Evolutionary Computation, pages 1320-1327, 2009. IEEE\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {A new preprocessing procedure for the haplotype inference problem.},\n type = {inproceedings},\n year = {2009},\n keywords = {dblp,isg_,isg_ehu},\n pages = {1320-1327},\n publisher = {IEEE},\n id = {6dd7a20e-528c-399b-9156-ba246dc5af7f},\n created = {2021-11-12T08:31:08.953Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:08.953Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Irurozki, Ekhine and Lozano, Jose A},\n booktitle = {IEEE Congress on Evolutionary Computation}\n}
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\n \n\n \n \n \n \n \n Guest Editorial: Special Issue on Evolutionary Algorithms Based on Probabilistic Models.\n \n \n \n\n\n \n Lozano, J., A.; Zhang, Q.; and Larrañaga, P.\n\n\n \n\n\n\n IEEE Transactions on Evolutionary Computation, 13(6): 1197-1198. 2009.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Guest Editorial: Special Issue on Evolutionary Algorithms Based on Probabilistic Models},\n type = {article},\n year = {2009},\n keywords = {isg_ehu,isg_jcr},\n pages = {1197-1198},\n volume = {13},\n id = {5f17fbf1-3a50-3b6a-95d8-c245903c1380},\n created = {2021-11-12T08:31:14.928Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:14.928Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Lozano, Jose A and Zhang, Qingfu and Larrañaga, Pedro},\n journal = {IEEE Transactions on Evolutionary Computation},\n number = {6}\n}
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\n \n\n \n \n \n \n \n Consensus policies to solve bioinformatic problems through Bayesian network classifiers and estimation of distribution algorithms.\n \n \n \n\n\n \n Armañanzas Arnedillo, R.\n\n\n \n\n\n\n Ph.D. Thesis, 2009.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@phdthesis{\n title = {Consensus policies to solve bioinformatic problems through Bayesian network classifiers and estimation of distribution algorithms},\n type = {phdthesis},\n year = {2009},\n institution = {University of the Basque Country},\n id = {3742159d-bb68-374d-b6bd-5dd4ec62f0dc},\n created = {2021-11-12T08:31:24.889Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:24.889Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {phdthesis},\n private_publication = {false},\n bibtype = {phdthesis},\n author = {Armañanzas Arnedillo, Rubén}\n}
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\n \n\n \n \n \n \n \n Full-system simulation of distributed memory multicomputers.\n \n \n \n\n\n \n Ridruejo, F., J.; Miguel-Alonso, J.; and Navaridas, J.\n\n\n \n\n\n\n Cluster Computing, 12(3): 309-322. 2009.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Full-system simulation of distributed memory multicomputers},\n type = {article},\n year = {2009},\n keywords = {isg_ehu,isg_jcr},\n pages = {309-322},\n volume = {12},\n id = {630e3bb6-cfa7-39cc-9d8b-32367211ad8e},\n created = {2021-11-12T08:31:31.909Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:31.909Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Ridruejo, Francisco J and Miguel-Alonso, José and Navaridas, Javier},\n journal = {Cluster Computing},\n number = {3}\n}
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\n \n\n \n \n \n \n \n Interconnection Network Simulation Using Traces of MPI Applications.\n \n \n \n\n\n \n Miguel-Alonso, J.; Navaridas, J.; and Perez, F., J.\n\n\n \n\n\n\n International Journal of Parallel Programming, 37(2): 153-174. 2009.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Interconnection Network Simulation Using Traces of MPI Applications},\n type = {article},\n year = {2009},\n keywords = {isg_ehu,isg_jcr},\n pages = {153-174},\n volume = {37},\n id = {c96e19ae-b687-3668-8b73-66e125199e91},\n created = {2021-11-12T08:31:34.784Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:34.784Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Miguel-Alonso, José and Navaridas, Javier and Perez, Francisco J},\n journal = {International Journal of Parallel Programming},\n number = {2}\n}
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\n \n\n \n \n \n \n \n Performance Evaluation of Interconnection Networks using Simulation: Tools and Case Studies.\n \n \n \n\n\n \n Navaridas, J.\n\n\n \n\n\n\n Ph.D. Thesis, 2009.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@phdthesis{\n title = {Performance Evaluation of Interconnection Networks using Simulation: Tools and Case Studies},\n type = {phdthesis},\n year = {2009},\n institution = {University of the Basque Country},\n id = {fb13c7c9-1b76-380c-88bc-c509deeb1a32},\n created = {2021-11-12T08:31:37.601Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:37.601Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {phdthesis},\n private_publication = {false},\n bibtype = {phdthesis},\n author = {Navaridas, Javier}\n}
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\n \n\n \n \n \n \n \n On the application of estimation of distribution algorithms to multi-marker tagging SNP selection.\n \n \n \n\n\n \n Santana, R.; Mendiburu, A.; Zaitlen, N.; Eskin, E.; and Lozano, J., A.\n\n\n \n\n\n\n Technical Report Department of Computer Science and Artificial Intelligence, University of the Basque Country, 2009.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@techreport{\n title = {On the application of estimation of distribution algorithms to multi-marker tagging SNP selection},\n type = {techreport},\n year = {2009},\n keywords = {isg_ehu,isg_jcr},\n issue = {EHU-KZAA-IK-4/09},\n institution = {Department of Computer Science and Artificial Intelligence, University of the Basque Country},\n id = {2d4d6709-e2f3-3641-8b6d-62b4e2d2f3ee},\n created = {2021-11-12T08:31:40.102Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:40.102Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {techreport},\n private_publication = {false},\n abstract = {This paper presents an algorithm for the automatic selection of a minimal subset of tagging single nucleotide polymorphisms (SNPs) using an estimation of distribution algorithm (EDA). The EDA stochastically searches the constrained space of possible feasible solutions and takes advantage of the underlying topological structure defined by the SNP correlations to model the problem interactions. The algorithm is evaluated across the HapMap reference panel data sets. The introduced algorithm is effective for the identification of minimal multi-marker SNP sets, which considerably reduce the dimension of the tagging SNP set in comparison with single-marker sets. New reduced tagging sets are obtained for all the HapMap SNP regions considered. We also show that the information extracted from the interaction graph representing the correlations between the SNPs can help to improve the efficiency of the optimization algorithm.},\n bibtype = {techreport},\n author = {Santana, R and Mendiburu, A and Zaitlen, N and Eskin, E and Lozano, J A}\n}
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\n This paper presents an algorithm for the automatic selection of a minimal subset of tagging single nucleotide polymorphisms (SNPs) using an estimation of distribution algorithm (EDA). The EDA stochastically searches the constrained space of possible feasible solutions and takes advantage of the underlying topological structure defined by the SNP correlations to model the problem interactions. The algorithm is evaluated across the HapMap reference panel data sets. The introduced algorithm is effective for the identification of minimal multi-marker SNP sets, which considerably reduce the dimension of the tagging SNP set in comparison with single-marker sets. New reduced tagging sets are obtained for all the HapMap SNP regions considered. We also show that the information extracted from the interaction graph representing the correlations between the SNPs can help to improve the efficiency of the optimization algorithm.\n
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\n \n\n \n \n \n \n \n Differential micro RNA expression in PBMC from multiple sclerosis patients.\n \n \n \n\n\n \n Otaegui, D.; Baranzini, S., E.; Armañanzas, R.; Calvo, B.; Muñoz-Culla, M.; Khankhanian, P.; Inza, I.; Lozano, J., A.; Castillo-Triviño, T.; Asensio, A.; Olaskoaga, J.; and Munain, A., L.\n\n\n \n\n\n\n PLoS ONE, 4(7). 2009.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Differential micro RNA expression in PBMC from multiple sclerosis patients},\n type = {article},\n year = {2009},\n keywords = {isg_ehu,isg_jcr},\n volume = {4},\n id = {9b994306-d656-3736-a2b4-036fab1566c2},\n created = {2021-11-12T08:31:58.161Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:58.161Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Otaegui, David and Baranzini, Sergio E and Armañanzas, Rubén and Calvo, Borja and Muñoz-Culla, Maider and Khankhanian, Puya and Inza, Iñaki and Lozano, Jose A and Castillo-Triviño, Tamara and Asensio, Ana and Olaskoaga, Javier and Munain, Adolfo L},\n journal = {PLoS ONE},\n number = {7}\n}
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\n  \n 2008\n \n \n (22)\n \n \n
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\n \n\n \n \n \n \n \n Protein folding in simplified models with estimation of distribution algorithms.\n \n \n \n\n\n \n Santana, R.; Larrañaga, P.; and Lozano, J., A.\n\n\n \n\n\n\n IEEE Transactions on Evolutionary Computation, 12(4): 418-438. 2008.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Protein folding in simplified models with estimation of distribution algorithms},\n type = {article},\n year = {2008},\n keywords = {isg_ehu,isg_jcr},\n pages = {418-438},\n volume = {12},\n id = {d8614fd3-5757-3636-a636-8dee20ca6e85},\n created = {2021-11-12T08:30:07.585Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:07.585Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {Simplified lattice models have played an important role in protein structure prediction and protein folding problems. These models can be useful for an initial approximation of the protein structure, and for the investigation of the dynamics that govern the protein folding process. Estimation of distribution algorithms (EDAs) are efficient evolutionary algorithms that can learn and exploit the search space regularities in the form of probabilistic dependencies. This paper introduces the application of different variants of EDAs to the solution of the protein structure prediction problem in simplified models, and proposes their use as a simulation tool for the analysis of the protein folding process. We develop new ideas for the application of EDAs to the bidimensional and tridimensional (2-d and 3-d) simplified protein folding problems. This paper analyzes the rationale behind the application of EDAs to these problems, and elucidates the relation ship bet ween our proposal and other population-based approaches proposed for the protein folding problem. We argue that EDAs are an efficient alternative for many instances of the protein structure prediction problem and are indeed appropriate for a theoretical analysis of search procedures in lattice models. All the algorithms introduced are tested on a set of difficult 2-d and 3-d instances from lattice models. Some of the results obtained with EDAs are superior to the ones obtained with other well-known population-based optimization algorithms.},\n bibtype = {article},\n author = {Santana, R and Larrañaga, P and Lozano, J A},\n journal = {IEEE Transactions on Evolutionary Computation},\n number = {4}\n}
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\n Simplified lattice models have played an important role in protein structure prediction and protein folding problems. These models can be useful for an initial approximation of the protein structure, and for the investigation of the dynamics that govern the protein folding process. Estimation of distribution algorithms (EDAs) are efficient evolutionary algorithms that can learn and exploit the search space regularities in the form of probabilistic dependencies. This paper introduces the application of different variants of EDAs to the solution of the protein structure prediction problem in simplified models, and proposes their use as a simulation tool for the analysis of the protein folding process. We develop new ideas for the application of EDAs to the bidimensional and tridimensional (2-d and 3-d) simplified protein folding problems. This paper analyzes the rationale behind the application of EDAs to these problems, and elucidates the relation ship bet ween our proposal and other population-based approaches proposed for the protein folding problem. We argue that EDAs are an efficient alternative for many instances of the protein structure prediction problem and are indeed appropriate for a theoretical analysis of search procedures in lattice models. All the algorithms introduced are tested on a set of difficult 2-d and 3-d instances from lattice models. Some of the results obtained with EDAs are superior to the ones obtained with other well-known population-based optimization algorithms.\n
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\n \n\n \n \n \n \n \n Inference of Population Structure Using Genetic Markers and a Bayesian Model Averaging Approach for Clustering.\n \n \n \n\n\n \n Santafé, G.; Lozano, J., A.; and Larrañaga, P.\n\n\n \n\n\n\n Journal of Computational Biology, 15(2): 207-220. 2008.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Inference of Population Structure Using Genetic Markers and a Bayesian Model Averaging Approach for Clustering},\n type = {article},\n year = {2008},\n keywords = {isg_ehu,isg_jcr},\n pages = {207-220},\n volume = {15},\n id = {7b3dac7c-f3f0-33a7-ab44-c91ed54ac21d},\n created = {2021-11-12T08:30:10.132Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:10.132Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Santafé, Guzmán and Lozano, Jose A and Larrañaga, Pedro},\n journal = {Journal of Computational Biology},\n number = {2}\n}
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\n \n\n \n \n \n \n \n Gene expression profiling in limb-girdle muscular dystrophy 2A.\n \n \n \n\n\n \n Sáenz, A.; Azpitarte, M.; Armañanzas, R.; Leturcq, F.; Alzualde, A.; Inza, I.; Garc\\'\\ia-Bragado, F.; la Herran, G.; Corcuera, J.; Cabello, A.; Navarro, C.; la Torre, C.; Gallardo, E.; Illa, I.; and de Munain, A.\n\n\n \n\n\n\n PLoS ONE, 3(11). 2008.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Gene expression profiling in limb-girdle muscular dystrophy 2A},\n type = {article},\n year = {2008},\n keywords = {isg_ehu,isg_jcr},\n volume = {3},\n id = {ea53fa72-2eef-3be3-973b-52aca3d12cbd},\n created = {2021-11-12T08:30:12.051Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:12.051Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Sáenz, Amets and Azpitarte, Margarita and Armañanzas, Rubén and Leturcq, France and Alzualde, Ainhoa and Inza, Iñaki and Garc\\'\\ia-Bragado, Federico and la Herran, Gaspar and Corcuera, Julián and Cabello, Ana and Navarro, Carmen and la Torre, Carolina and Gallardo, Eduardo and Illa, Isabel and de Munain, Adolfo},\n journal = {PLoS ONE},\n number = {11}\n}
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\n \n\n \n \n \n \n \n An empirical analysis of loopy belief propagation in three topologies: Grids, small-world networks and random graphs.\n \n \n \n\n\n \n Santana, R.; Mendiburu, A.; and Lozano, J., A.\n\n\n \n\n\n\n In Jaeger, M.; and Nielsen, T., D., editor(s), Proceedings of the Fourth European Workshop on Probabilistic Graphical Models (PGM-2008), pages 249-256, 2008. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {An empirical analysis of loopy belief propagation in three topologies: Grids, small-world networks and random graphs},\n type = {inproceedings},\n year = {2008},\n keywords = {isg_ehu},\n pages = {249-256},\n id = {855836ac-2672-3397-b539-7148af2b0bab},\n created = {2021-11-12T08:30:13.423Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:13.423Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {Recently, much research has been devoted to the study of loopy belief propagation algorithm. However, little attention has been paid to the change of its behavior in relation with the problem graph topology. In this paper we empirically study the behavior of loopy belief propagation on different network topologies which include grids, small-world networks and random graphs. In our experiments, several descriptors of the algorithm are collected in order to analyze its behavior. We show that the performance of the algorithm is highly sensitive to changes in the topologies. Furthermore, evidence is given showing that the addition of shortcuts to grids can determine important changes in the dynamics of the algorithm.},\n bibtype = {inproceedings},\n author = {Santana, R and Mendiburu, A and Lozano, J A},\n editor = {Jaeger, Manfred and Nielsen, Thomas D},\n booktitle = {Proceedings of the Fourth European Workshop on Probabilistic Graphical Models (PGM-2008)}\n}
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\n Recently, much research has been devoted to the study of loopy belief propagation algorithm. However, little attention has been paid to the change of its behavior in relation with the problem graph topology. In this paper we empirically study the behavior of loopy belief propagation on different network topologies which include grids, small-world networks and random graphs. In our experiments, several descriptors of the algorithm are collected in order to analyze its behavior. We show that the performance of the algorithm is highly sensitive to changes in the topologies. Furthermore, evidence is given showing that the addition of shortcuts to grids can determine important changes in the dynamics of the algorithm.\n
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\n \n\n \n \n \n \n \n Adaptive and Multilevel Metaheuristics.\n \n \n \n\n\n \n Santana, R.; Larrañaga, P.; and Lozano, J., A.\n\n\n \n\n\n\n Volume 136 of Studies in Computational Intelligence. pages 177-197. Cotta, C.; Sevaux, M.; and Sörensen, K., editor(s). Springer, 2008.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inbook{\n type = {inbook},\n year = {2008},\n keywords = {isg_ehu},\n pages = {177-197},\n volume = {136},\n publisher = {Springer},\n series = {Studies in Computational Intelligence},\n chapter = {Adaptive and Multilevel Metaheuristics},\n id = {a2a611d4-6cb3-3245-8b0e-e68cf6470a56},\n created = {2021-11-12T08:30:14.581Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:14.581Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {incollection},\n private_publication = {false},\n abstract = {Estimation of distribution algorithms (EDAs) are evolutionary methods that use probabilistic models instead of genetic operators to lead the search. Most of current proposals on EDAs do not incorporate adaptive techniques. Usually, the class of probabilistic model employed as well as the learning and sampling methods are static. In this paper, we present a general framework for introducing adaptation in EDAs. This framework allows the possibility of changing the class of probabilistic models during the evolution. We present a number of measures, and techniques that can be used to evaluate the effect of the EDA components in order to design adaptive EDAs. As a case of study we present an adaptive EDA that combines different classes of probabilistic models and sampling methods. The algorithm is evaluated in the solution of the satisfiability problem..},\n bibtype = {inbook},\n author = {Santana, R and Larrañaga, Pedro and Lozano, J A},\n editor = {Cotta, C and Sevaux, M and Sörensen, K}\n}
\n
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\n Estimation of distribution algorithms (EDAs) are evolutionary methods that use probabilistic models instead of genetic operators to lead the search. Most of current proposals on EDAs do not incorporate adaptive techniques. Usually, the class of probabilistic model employed as well as the learning and sampling methods are static. In this paper, we present a general framework for introducing adaptation in EDAs. This framework allows the possibility of changing the class of probabilistic models during the evolution. We present a number of measures, and techniques that can be used to evaluate the effect of the EDA components in order to design adaptive EDAs. As a case of study we present an adaptive EDA that combines different classes of probabilistic models and sampling methods. The algorithm is evaluated in the solution of the satisfiability problem..\n
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\n \n\n \n \n \n \n \n Prioritization of candidate cancer genes—an aid to oncogenomic studies.\n \n \n \n\n\n \n Furney, S., J.; Calvo, B.; Larrañaga, P.; Lozano, J., A.; and Lopez-Bigas, N.\n\n\n \n\n\n\n Nucleic acids research, 36(18): e115. 2008.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Prioritization of candidate cancer genes—an aid to oncogenomic studies},\n type = {article},\n year = {2008},\n keywords = {isg_ehu,isg_jcr},\n pages = {e115},\n volume = {36},\n publisher = {Oxford University Press},\n id = {09543e32-28c0-37e6-a391-98fb4a9b9c5a},\n created = {2021-11-12T08:30:15.985Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:15.985Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Furney, Simon J and Calvo, Borja and Larrañaga, Pedro and Lozano, Jose A and Lopez-Bigas, Nuria},\n journal = {Nucleic acids research},\n number = {18}\n}
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\n \n\n \n \n \n \n \n Dynamic Search Space Transformations for Software Test Data Generation.\n \n \n \n\n\n \n Sagarna, R.; and Lozano, J., A.\n\n\n \n\n\n\n Computational Intelligence, 24(1): 23-61. 2008.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Dynamic Search Space Transformations for Software Test Data Generation},\n type = {article},\n year = {2008},\n keywords = {isg_ehu,isg_jcr},\n pages = {23-61},\n volume = {24},\n id = {3bc1beb1-3202-3224-b965-c0037c2ff54b},\n created = {2021-11-12T08:30:22.888Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:22.888Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Sagarna, Ramón and Lozano, Jose A},\n journal = {Computational Intelligence},\n number = {1}\n}
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\n \n\n \n \n \n \n \n Selection of human embryos for transfer by Bayesian classifiers.\n \n \n \n\n\n \n Morales, D., A.; Bengoetxea, E.; and Larrañaga, P.\n\n\n \n\n\n\n Computers in biology and medicine, 38(11-12): 1177-1186. 5 2008.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Selection of human embryos for transfer by Bayesian classifiers.},\n type = {article},\n year = {2008},\n pages = {1177-1186},\n volume = {38},\n month = {5},\n id = {4b70ff09-4830-330b-8dc2-40dcfdbdd324},\n created = {2021-11-12T08:30:25.946Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:25.946Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {In this work we approach by Bayesian classifiers the selection of human embryos from images. This problem consists of choosing the embryos to be transferred in human-assisted reproduction treatments, which Bayesian classifiers address as a supervised classification problem. Different Bayesian classifiers capable of taking into account diverse dependencies between variables of this problem are tested in order to analyse their performance and validity for building a potential decision support system. The analysis by receiver operating characteristic (ROC) proves that the Bayesian classifiers presented in this paper are an appropriated and robust approach for this aim. From the Bayesian classifiers tested, the tree augmented naive Bayes, k-dependence Bayesian and naive Bayes classifiers showed to perform almost as well as the semi naive Bayes and selective naive Bayes classifiers.},\n bibtype = {article},\n author = {Morales, Dinora A and Bengoetxea, Endika and Larrañaga, Pedro},\n journal = {Computers in biology and medicine},\n number = {11-12}\n}
\n
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\n In this work we approach by Bayesian classifiers the selection of human embryos from images. This problem consists of choosing the embryos to be transferred in human-assisted reproduction treatments, which Bayesian classifiers address as a supervised classification problem. Different Bayesian classifiers capable of taking into account diverse dependencies between variables of this problem are tested in order to analyse their performance and validity for building a potential decision support system. The analysis by receiver operating characteristic (ROC) proves that the Bayesian classifiers presented in this paper are an appropriated and robust approach for this aim. From the Bayesian classifiers tested, the tree augmented naive Bayes, k-dependence Bayesian and naive Bayes classifiers showed to perform almost as well as the semi naive Bayes and selective naive Bayes classifiers.\n
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\n \n\n \n \n \n \n \n How trustworthy is CRAFTY'S analysis of world chess champions?.\n \n \n \n\n\n \n Guid, M.; Pérez, A.; and Bratko, I.\n\n\n \n\n\n\n ICGA journal, 31: 131-144. 2008.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {How trustworthy is CRAFTY'S analysis of world chess champions?},\n type = {article},\n year = {2008},\n keywords = {isg_ehu,isg_jcr},\n pages = {131-144},\n volume = {31},\n id = {b044982a-edfa-3f7e-a147-5a06d7d678d6},\n created = {2021-11-12T08:30:37.286Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:37.286Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Guid, Matej and Pérez, Aritz and Bratko, Ivan},\n journal = {ICGA journal}\n}
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\n \n\n \n \n \n \n \n Combining Variable Neighborhood Search and Estimation of Distribution Algorithms in the Protein Side Chain Placement Problem.\n \n \n \n\n\n \n Santana, R.; Larrañaga, P.; and Lozano, J., A.\n\n\n \n\n\n\n Journal of Heuristics, 14: 519-547. 2008.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Combining Variable Neighborhood Search and Estimation of Distribution Algorithms in the Protein Side Chain Placement Problem},\n type = {article},\n year = {2008},\n keywords = {isg_ehu,isg_jcr},\n pages = {519-547},\n volume = {14},\n id = {28b62ea5-f17e-380b-aa39-4eb327824e02},\n created = {2021-11-12T08:30:39.743Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:39.743Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {The aim of this work is to introduce several proposals for combining two metaheuristics: variable neighborhood search (VNS) and estimation of distribution algorithms (EDAs). Although each of these metaheuristics has been previously hybridized in several ways, this paper constitutes the first attempt to combine both optimization methods. The different ways of combining VNS and EDAs will be classified into three groups. In the first group, we will consider combinations where the philosophy underlying VNS is embedded in EDAs. Considering different neighborhood spaces (points, populations or probability distributions), we will obtain instantiations for the approaches in this group. The second group of algorithms is obtained when probabilistic models (or any other machine learning paradigm) are used in order to exploit the good and bad shakes of the randomly generated solutions in a reduced variable neighborhood search. The last group of algorithms co ntains t he results of alternating VNS and EDAs. An application of the first approach is presented in the protein side chain placement problem. The results obtained show the superiority of the hybrid algorithm in comparison with EDAs and VNS.},\n bibtype = {article},\n author = {Santana, R and Larrañaga, P and Lozano, J A},\n journal = {Journal of Heuristics}\n}
\n
\n\n\n
\n The aim of this work is to introduce several proposals for combining two metaheuristics: variable neighborhood search (VNS) and estimation of distribution algorithms (EDAs). Although each of these metaheuristics has been previously hybridized in several ways, this paper constitutes the first attempt to combine both optimization methods. The different ways of combining VNS and EDAs will be classified into three groups. In the first group, we will consider combinations where the philosophy underlying VNS is embedded in EDAs. Considering different neighborhood spaces (points, populations or probability distributions), we will obtain instantiations for the approaches in this group. The second group of algorithms is obtained when probabilistic models (or any other machine learning paradigm) are used in order to exploit the good and bad shakes of the randomly generated solutions in a reduced variable neighborhood search. The last group of algorithms co ntains t he results of alternating VNS and EDAs. An application of the first approach is presented in the protein side chain placement problem. The results obtained show the superiority of the hybrid algorithm in comparison with EDAs and VNS.\n
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\n \n\n \n \n \n \n \n Positive Unlabelled Learning with Applications in Computational Biology.\n \n \n \n\n\n \n Calvo, B.\n\n\n \n\n\n\n Ph.D. Thesis, 2008.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@phdthesis{\n title = {Positive Unlabelled Learning with Applications in Computational Biology},\n type = {phdthesis},\n year = {2008},\n institution = {University of the Basque Country},\n id = {273f97df-a643-3cf1-aa01-0c6b3c98ff6a},\n created = {2021-11-12T08:30:45.978Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:45.978Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {phdthesis},\n private_publication = {false},\n bibtype = {phdthesis},\n author = {Calvo, Borja}\n}
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\n \n\n \n \n \n \n \n A review of estimation of distribution algorithms in bioinformatics.\n \n \n \n\n\n \n Armañanzas, R.; Inza, I.; Santana, R.; Saeys, Y.; Flores, J., L.; Lozano, J., A.; de Peer, Y.; Blanco, R.; Robles, V.; Bielza, C.; and Larrañaga, P.\n\n\n \n\n\n\n BioData mining, 1(1): 6. 5 2008.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {A review of estimation of distribution algorithms in bioinformatics.},\n type = {article},\n year = {2008},\n pages = {6},\n volume = {1},\n month = {5},\n id = {cd54ef26-0aeb-3932-83bb-488146bc1bab},\n created = {2021-11-12T08:30:47.475Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:47.475Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {Evolutionary search algorithms have become an essential asset in the algorithmic toolbox for solving high-dimensional optimization problems in across a broad range of bioinformatics problems. Genetic algorithms, the most well-known and representative evolutionary search technique, have been the subject of the major part of such applications. Estimation of distribution algorithms (EDAs) offer a novel evolutionary paradigm that constitutes a natural and attractive alternative to genetic algorithms. They make use of a probabilistic model, learnt from the promising solutions, to guide the search process. In this paper, we set out a basic taxonomy of EDA techniques, underlining the nature and complexity of the probabilistic model of each EDA variant. We review a set of innovative works that make use of EDA techniques to solve challenging bioinformatics problems, emphasizing the EDA paradigm's potential for further research in this domain.},\n bibtype = {article},\n author = {Armañanzas, Rubén and Inza, Iñaki and Santana, Roberto and Saeys, Yvan and Flores, Jose Luis and Lozano, Jose Antonio and de Peer, Yves and Blanco, Rosa and Robles, Víctor and Bielza, Concha and Larrañaga, Pedro},\n journal = {BioData mining},\n number = {1}\n}
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\n Evolutionary search algorithms have become an essential asset in the algorithmic toolbox for solving high-dimensional optimization problems in across a broad range of bioinformatics problems. Genetic algorithms, the most well-known and representative evolutionary search technique, have been the subject of the major part of such applications. Estimation of distribution algorithms (EDAs) offer a novel evolutionary paradigm that constitutes a natural and attractive alternative to genetic algorithms. They make use of a probabilistic model, learnt from the promising solutions, to guide the search process. In this paper, we set out a basic taxonomy of EDA techniques, underlining the nature and complexity of the probabilistic model of each EDA variant. We review a set of innovative works that make use of EDA techniques to solve challenging bioinformatics problems, emphasizing the EDA paradigm's potential for further research in this domain.\n
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\n \n\n \n \n \n \n \n Estimation of distribution algorithms with affinity propagation methods.\n \n \n \n\n\n \n Santana, R.; Larrañaga, P.; and Lozano, J., A.\n\n\n \n\n\n\n Technical Report Department of Computer Science and Artificial Intelligence, University of the Basque Country, 2008.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@techreport{\n title = {Estimation of distribution algorithms with affinity propagation methods},\n type = {techreport},\n year = {2008},\n keywords = {isg_ehu,isg_jcr},\n issue = {EHU-KZAA-IK-1/08},\n institution = {Department of Computer Science and Artificial Intelligence, University of the Basque Country},\n id = {14749663-349f-3a5d-9c16-a54ef3245013},\n created = {2021-11-12T08:30:52.623Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:52.623Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {techreport},\n private_publication = {false},\n abstract = {Estimation of distribution algorithms (EDAs) that use marginal product model factorizations have been widely applied to a broad range of, mainly binary, optimization problems.In this paper, we introduce the affinity propagation EDA which learns a marginal product model by clustering a matrix of mutual information learned from the data using a very efficient message-passing algorithm known as affinity propagation. The introduced algorithm is tested on a set of binary and non-binary decomposable functions and using a hard combinatorial class of problem known as the HP protein model. The results show that the algorithm is a very efficient alternative to other EDAs that use marginal product model factorizations such as the extended compact genetic algorithm (ECGA) and improves the quality of the results achieved by ECGA when the cardinality of the variables is increased.},\n bibtype = {techreport},\n author = {Santana, R and Larrañaga, P and Lozano, J A}\n}
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\n Estimation of distribution algorithms (EDAs) that use marginal product model factorizations have been widely applied to a broad range of, mainly binary, optimization problems.In this paper, we introduce the affinity propagation EDA which learns a marginal product model by clustering a matrix of mutual information learned from the data using a very efficient message-passing algorithm known as affinity propagation. The introduced algorithm is tested on a set of binary and non-binary decomposable functions and using a hard combinatorial class of problem known as the HP protein model. The results show that the algorithm is a very efficient alternative to other EDAs that use marginal product model factorizations such as the extended compact genetic algorithm (ECGA) and improves the quality of the results achieved by ECGA when the cardinality of the variables is increased.\n
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\n \n\n \n \n \n \n \n Component weighting functions for adaptive search with EDAs.\n \n \n \n\n\n \n Santana, R.; Larrañaga, P.; and Lozano, J., A.\n\n\n \n\n\n\n In Proceedings of the 2008 Congress on Evolutionary Computation CEC-2008, pages 4067-4074, 2008. IEEE Press\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Component weighting functions for adaptive search with EDAs},\n type = {inproceedings},\n year = {2008},\n keywords = {isg_ehu},\n pages = {4067-4074},\n publisher = {IEEE Press},\n id = {9c3b8ea9-5f7b-3a3a-997c-221a5880ccdc},\n created = {2021-11-12T08:30:54.789Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:54.789Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {This paper introduces the component weighting approach as a general optimization heuristic to increase the likelihood of escaping from local optima by dynamically modifying the fitness function. The approach is tested on the optimization of the simplified hydrophobic-polar (HP) protein problem using estimation of distribution algorithms (EDAs). We show that the use of component weighting together with statistical information extracted from the set of selected solutions considerably improve the results of EDAs for the HP problem. The paper also elaborates on the use of probabilistic modeling for the definition of dynamic fitness functions and on the use of combinations of models.},\n bibtype = {inproceedings},\n author = {Santana, R and Larrañaga, P and Lozano, J A},\n booktitle = {Proceedings of the 2008 Congress on Evolutionary Computation CEC-2008}\n}
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\n This paper introduces the component weighting approach as a general optimization heuristic to increase the likelihood of escaping from local optima by dynamically modifying the fitness function. The approach is tested on the optimization of the simplified hydrophobic-polar (HP) protein problem using estimation of distribution algorithms (EDAs). We show that the use of component weighting together with statistical information extracted from the set of selected solutions considerably improve the results of EDAs for the HP problem. The paper also elaborates on the use of probabilistic modeling for the definition of dynamic fitness functions and on the use of combinations of models.\n
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\n \n\n \n \n \n \n \n Bayesian classification for the selection of in vitro human embryos using morphological and clinical data.\n \n \n \n\n\n \n Morales, D.; Bengoetxea, E.; Larrañaga, P.; Garc\\'\\ia, M.; Franco, Y.; Fresnada, M.; and Merino, M.\n\n\n \n\n\n\n Computer Methods and Programs in Biomedicine, 90(2): 104-116. 2008.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Bayesian classification for the selection of in vitro human embryos using morphological and clinical data},\n type = {article},\n year = {2008},\n keywords = {isg_ehu,isg_jcr},\n pages = {104-116},\n volume = {90},\n id = {50d03eae-3ec6-318a-9163-f2e7f1eead5c},\n created = {2021-11-12T08:31:01.110Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:01.110Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Morales, Dinora and Bengoetxea, Endika and Larrañaga, Pedro and Garc\\'\\ia, Miguel and Franco, Yosu and Fresnada, Mónica and Merino, Marisa},\n journal = {Computer Methods and Programs in Biomedicine},\n number = {2}\n}
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\n \n\n \n \n \n \n \n The impact of probabilistic learning algorithms in EDAs based on Bayesian networks.\n \n \n \n\n\n \n Echegoyen, C.; Santana, R.; Lozano, J., A.; and Larrañaga, P.\n\n\n \n\n\n\n In Linkage in Evolutionary Computation, of Studies in Computational Intelligence, pages 109-139, 2008. Springer\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@inproceedings{\n title = {The impact of probabilistic learning algorithms in EDAs based on Bayesian networks},\n type = {inproceedings},\n year = {2008},\n keywords = {isg_ehu},\n pages = {109-139},\n publisher = {Springer},\n series = {Studies in Computational Intelligence},\n id = {2a35c6dc-8e5a-3c3c-8d74-b1c82274e96d},\n created = {2021-11-12T08:31:02.448Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:02.448Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {This paper discusses exact learning of Bayesian networks in estimation of distribution algorithms. The estimation of Bayesian network algorithm (EBNA) is used to analyze the impact of learning the optimal (exact) structure in the search. By applying recently introduced methods that allow learning optimal Bayesian networks, we investigate two important issues in EDAs. First, we analyze the question of whether learning more accurate (exact) models of the dependencies implies a better performance of EDAs. Secondly, we are able to study the way in which the problem structure is translated into the probabilistic model when exact learning is accomplished. The results obtained reveal that the quality of the problem information captured by the probability model can improve when the accuracy of the learning algorithm employed is increased. However, improvements in model accuracy do not always imply a more efficient search.},\n bibtype = {inproceedings},\n author = {Echegoyen, C and Santana, R and Lozano, J A and Larrañaga, P},\n booktitle = {Linkage in Evolutionary Computation}\n}
\n
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\n This paper discusses exact learning of Bayesian networks in estimation of distribution algorithms. The estimation of Bayesian network algorithm (EBNA) is used to analyze the impact of learning the optimal (exact) structure in the search. By applying recently introduced methods that allow learning optimal Bayesian networks, we investigate two important issues in EDAs. First, we analyze the question of whether learning more accurate (exact) models of the dependencies implies a better performance of EDAs. Secondly, we are able to study the way in which the problem structure is translated into the probabilistic model when exact learning is accomplished. The results obtained reveal that the quality of the problem information captured by the probability model can improve when the accuracy of the learning algorithm employed is increased. However, improvements in model accuracy do not always imply a more efficient search.\n
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\n \n\n \n \n \n \n \n Advances on Supervised and Unsupervised Learning of Bayesian Network Models. Application to Population Genetics.\n \n \n \n\n\n \n Santafé, G.\n\n\n \n\n\n\n Ph.D. Thesis, 2008.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@phdthesis{\n title = {Advances on Supervised and Unsupervised Learning of Bayesian Network Models. Application to Population Genetics},\n type = {phdthesis},\n year = {2008},\n institution = {University of the Basque Country},\n id = {eb36322d-34bb-3fa1-83be-fb539e788cba},\n created = {2021-11-12T08:31:03.280Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:03.280Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {phdthesis},\n private_publication = {false},\n bibtype = {phdthesis},\n author = {Santafé, Guzmán}\n}
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\n \n\n \n \n \n \n \n An EDA based on local Markov property and Gibbs sampling.\n \n \n \n\n\n \n Shakya, S.; and Santana, R.\n\n\n \n\n\n\n In Keijzer, M., editor(s), Proceedings of the 2008 Genetic and evolutionary computation conference (GECCO), pages 475-476, 2008. ACM\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {An EDA based on local Markov property and Gibbs sampling},\n type = {inproceedings},\n year = {2008},\n keywords = {isg_ehu},\n pages = {475-476},\n publisher = {ACM},\n id = {5d9d9c23-3509-3175-bcee-ee5867c249e7},\n created = {2021-11-12T08:31:09.223Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:09.223Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {The key ideas behind most of the recently proposed Markov networks based EDAs were to factorise the joint probability distribution in terms of the cliques in the undirected graph. As such, they made use of the global Markov property of the Markov network. Here we presents a Markov Network based EDA that exploits Gibbs sampling to sample from the Local Markov property, the Markovianity, and does not directly model the joint distribution. We call it Markovianity based Optimisation Algorithm. Some initial results on the performance of the proposed algorithm shows that it compares well with other Bayesian network based EDAs},\n bibtype = {inproceedings},\n author = {Shakya, Siddhartha and Santana, R},\n editor = {Keijzer, Maarten},\n booktitle = {Proceedings of the 2008 Genetic and evolutionary computation conference (GECCO)}\n}
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\n The key ideas behind most of the recently proposed Markov networks based EDAs were to factorise the joint probability distribution in terms of the cliques in the undirected graph. As such, they made use of the global Markov property of the Markov network. Here we presents a Markov Network based EDA that exploits Gibbs sampling to sample from the Local Markov property, the Markovianity, and does not directly model the joint distribution. We call it Markovianity based Optimisation Algorithm. Some initial results on the performance of the proposed algorithm shows that it compares well with other Bayesian network based EDAs\n
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\n \n\n \n \n \n \n \n Adding probabilistic dependencies to the search of protein side chain configurations using EDAs.\n \n \n \n\n\n \n Santana, R.; Larrañaga, P.; and Lozano, J., A.\n\n\n \n\n\n\n In Rudolph, G.; Jansen, T.; Lucas, S.; Poloni, C.; and Beume, N., editor(s), Parallel Problem Solving from Nature - PPSN X, volume 5199, of Lecture Notes in Computer Science, pages 1120-1129, 2008. Springer\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Adding probabilistic dependencies to the search of protein side chain configurations using EDAs},\n type = {inproceedings},\n year = {2008},\n keywords = {isg_ehu},\n pages = {1120-1129},\n volume = {5199},\n publisher = {Springer},\n series = {Lecture Notes in Computer Science},\n id = {3b8e6eea-5222-39d9-9e25-8c8196fd44a1},\n created = {2021-11-12T08:31:19.348Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:19.348Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {The problem of finding an optimal positioning for the side chain residues of a protein is called the side chain placement or side chain prediction problem. It can be posed as an optimization problem in the discrete domain. In this paper we use an estimation of distribution algorithm to address this optimization problem. Using a set of 50 difficult protein instances, it is shown that the addition of dependencies between the variables in the probabilistic model can improve the quality of the solutions achieved for most of the instances considered. However, we also show that only when information about the known interactions between the residues is considered in the creation of the probabilistic model, the addition of the dependencies contributes to improve the quality of the solutions obtained.},\n bibtype = {inproceedings},\n author = {Santana, R and Larrañaga, P and Lozano, J A},\n editor = {Rudolph, G and Jansen, T and Lucas, S and Poloni, C and Beume, N},\n booktitle = {Parallel Problem Solving from Nature - PPSN X}\n}
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\n The problem of finding an optimal positioning for the side chain residues of a protein is called the side chain placement or side chain prediction problem. It can be posed as an optimization problem in the discrete domain. In this paper we use an estimation of distribution algorithm to address this optimization problem. Using a set of 50 difficult protein instances, it is shown that the addition of dependencies between the variables in the probabilistic model can improve the quality of the solutions achieved for most of the instances considered. However, we also show that only when information about the known interactions between the residues is considered in the creation of the probabilistic model, the addition of the dependencies contributes to improve the quality of the solutions obtained.\n
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\n \n\n \n \n \n \n \n What is behind a summary-evaluation decision?.\n \n \n \n\n\n \n Zipitria, I.; Larrañaga, P.; Armañanzas, R.; Arruarte, A.; and Elorriaga, J.\n\n\n \n\n\n\n Behavior Research Methods, 40: 597-612. 2008.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {What is behind a summary-evaluation decision?},\n type = {article},\n year = {2008},\n keywords = {isg_ehu,isg_jcr},\n pages = {597-612},\n volume = {40},\n id = {87160309-905c-3536-8eb9-f9f9db6ff6da},\n created = {2021-11-12T08:31:41.221Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:41.221Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Zipitria, Iraide and Larrañaga, Pedro and Armañanzas, Rubén and Arruarte, Ana and Elorriaga, Jon},\n journal = {Behavior Research Methods}\n}
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\n \n\n \n \n \n \n \n Improving the performance of large interconnection networks using congestion-control mechanisms.\n \n \n \n\n\n \n Miguel-Alonso, J.; Izu, C.; and Gregorio, J.\n\n\n \n\n\n\n Performance Evaluation, 65(03/04/2016): 203-211. 2008.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Improving the performance of large interconnection networks using congestion-control mechanisms},\n type = {article},\n year = {2008},\n keywords = {isg_ehu,isg_jcr},\n pages = {203-211},\n volume = {65},\n id = {3c3e5d7a-f0b6-3c74-a7d9-8ae66ae733fc},\n created = {2021-11-12T08:31:43.468Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:43.468Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Miguel-Alonso, José and Izu, Cruz and Gregorio, José},\n journal = {Performance Evaluation},\n number = {03/04/2016}\n}
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\n \n\n \n \n \n \n \n Detecting reliable gene interactions by a hierarchy of Bayesian network classifiers.\n \n \n \n\n\n \n Armañanzas, R.; Inza, I.; and Larrañaga, P.\n\n\n \n\n\n\n Computer Methods and Programs in Biomedicine, 91(2). 2008.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Detecting reliable gene interactions by a hierarchy of Bayesian network classifiers},\n type = {article},\n year = {2008},\n keywords = {isg_ehu,isg_jcr},\n volume = {91},\n id = {5a9d48d7-d7e3-37ff-8703-b23147e6912f},\n created = {2021-11-12T08:32:00.320Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:00.320Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Armañanzas, Rubén and Inza, Iñaki and Larrañaga, Pedro},\n journal = {Computer Methods and Programs in Biomedicine},\n number = {2}\n}
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\n \n\n \n \n \n \n \n The role of a priori information in the minimization of contact potentials by means of estimation of distribution algorithms.\n \n \n \n\n\n \n Santana, R.; Larrañaga, P.; and Lozano, J., A.\n\n\n \n\n\n\n In Marchiori, E.; Moore, J., H.; and Rajapakse, J., C., editor(s), Proceedings of the Fifth European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, volume 4447, of Lecture Notes in Computer Science, pages 247-257, 2007. Springer\n \n\n\n\n
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@inproceedings{\n title = {The role of a priori information in the minimization of contact potentials by means of estimation of distribution algorithms},\n type = {inproceedings},\n year = {2007},\n keywords = {isg_ehu},\n pages = {247-257},\n volume = {4447},\n publisher = {Springer},\n series = {Lecture Notes in Computer Science},\n id = {2491c9b4-03eb-38dd-8457-a020b7281568},\n created = {2021-11-12T08:30:12.338Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:12.338Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {Directed search methods and probabilistic approaches have been used as two alternative ways for computational protein design. This paper presents a hybrid methodology that combines features from both approaches. Three estimation of distribution algorithms are applied to the solution of a protein design problem by minimization of contact potentials. The combination of probabilistic models able to represent probabilistic dependencies with the use of information about residues interactions in the protein contact graph is shown to improve the efficiency of search for the problems evaluated.},\n bibtype = {inproceedings},\n author = {Santana, R and Larrañaga, P and Lozano, J A},\n editor = {Marchiori, E and Moore, J H and Rajapakse, J C},\n booktitle = {Proceedings of the Fifth European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics}\n}
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\n Directed search methods and probabilistic approaches have been used as two alternative ways for computational protein design. This paper presents a hybrid methodology that combines features from both approaches. Three estimation of distribution algorithms are applied to the solution of a protein design problem by minimization of contact potentials. The combination of probabilistic models able to represent probabilistic dependencies with the use of information about residues interactions in the protein contact graph is shown to improve the efficiency of search for the problems evaluated.\n
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\n \n\n \n \n \n \n \n An Algorithm for Task-based Application Composition.\n \n \n \n\n\n \n Davidyuk, O.; Ceberio, J.; and Riekki, J.\n\n\n \n\n\n\n In Proceedings of the 11th IASTED International Conference on Software Engineering and Applications, of SEA '07, pages 465-472, 2007. ACTA Press\n \n\n\n\n
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@inproceedings{\n title = {An Algorithm for Task-based Application Composition},\n type = {inproceedings},\n year = {2007},\n keywords = {isg_ehu},\n pages = {465-472},\n publisher = {ACTA Press},\n series = {SEA '07},\n id = {e29d4356-9fc9-3090-a1b3-507bf0cd7690},\n created = {2021-11-12T08:30:17.859Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:17.859Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Davidyuk, Oleg and Ceberio, Josu and Riekki, Jukka},\n booktitle = {Proceedings of the 11th IASTED International Conference on Software Engineering and Applications}\n}
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\n \n\n \n \n \n \n \n An application allocation algorithm for pervasive environments.\n \n \n \n\n\n \n Davidyuk, O.; Riekki, J.; and Ceberio, J.\n\n\n \n\n\n\n In Proc. IADIS International Conference on Intelligent Systems and Agents (ISA'07), 2007. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {An application allocation algorithm for pervasive environments},\n type = {inproceedings},\n year = {2007},\n keywords = {isg_ehu},\n id = {fa0e42d5-1d4b-3a5f-806a-cc8c5329899d},\n created = {2021-11-12T08:30:21.759Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:21.759Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Davidyuk, Oleg and Riekki, Jukka and Ceberio, Josu},\n booktitle = {Proc. IADIS International Conference on Intelligent Systems and Agents (ISA'07)}\n}
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\n \n\n \n \n \n \n \n Optimization by max-propagation using Kikuchi approximations.\n \n \n \n\n\n \n Höns, R.; Santana, R.; Larrañaga, P.; and Lozano, J., A.\n\n\n \n\n\n\n Technical Report Department of Computer Science and Artificial Intelligence, University of the Basque Country, 2007.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@techreport{\n title = {Optimization by max-propagation using Kikuchi approximations},\n type = {techreport},\n year = {2007},\n keywords = {isg_ehu,isg_jcr},\n issue = {EHU-KZAA-IK-2/07},\n institution = {Department of Computer Science and Artificial Intelligence, University of the Basque Country},\n id = {3fe19e40-0a3d-3427-abba-557dc6354432},\n created = {2021-11-12T08:30:27.038Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:27.038Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {techreport},\n private_publication = {false},\n abstract = {In this paper we address the problem of using region-based approximations to find the optimal points of a given function. Our approach combines the use of Kikuchi approximations with the application of generalized belief propagation (GBP) using maximization instead of marginalization. The relationship between the fixed points of maximum GBP and the free energy is elucidated. A straightforward connection between the function to be optimized and the Kikuchi approximation (which holds only for maximum GBP, not for marginal GBP) is proven. Later, we show that maximum GBP can be combined with a dynamic programming algorithm to find the most probable configurations of a graphical model. We then analyze the dynamics of the procedure proposed and show how its different steps can be manipulated to influence the search for optimal solutions.},\n bibtype = {techreport},\n author = {Höns, R and Santana, R and Larrañaga, P and Lozano, J A}\n}
\n
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\n In this paper we address the problem of using region-based approximations to find the optimal points of a given function. Our approach combines the use of Kikuchi approximations with the application of generalized belief propagation (GBP) using maximization instead of marginalization. The relationship between the fixed points of maximum GBP and the free energy is elucidated. A straightforward connection between the function to be optimized and the Kikuchi approximation (which holds only for maximum GBP, not for marginal GBP) is proven. Later, we show that maximum GBP can be combined with a dynamic programming algorithm to find the most probable configurations of a graphical model. We then analyze the dynamics of the procedure proposed and show how its different steps can be manipulated to influence the search for optimal solutions.\n
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\n \n\n \n \n \n \n \n Wrapper discretization by means of estimation of distribution algorithms.\n \n \n \n\n\n \n Flores, J., L.; Inza, I.; and Larrañaga, P.\n\n\n \n\n\n\n Intelligent Data Analysis, 11(5): 525-545. 2007.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Wrapper discretization by means of estimation of distribution algorithms},\n type = {article},\n year = {2007},\n keywords = {isg_ehu,isg_jcr},\n pages = {525-545},\n volume = {11},\n id = {a2315225-e68d-3e0a-aa7b-36c766de926d},\n created = {2021-11-12T08:30:35.615Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:35.615Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Flores, Jose L and Inza, Iñaki and Larrañaga, Pedro},\n journal = {Intelligent Data Analysis},\n number = {5}\n}
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\n \n\n \n \n \n \n \n An Optimization Approach for Software Test Data Generation: Applications of Estimation of Distribution Algorithms and Scatter Search.\n \n \n \n\n\n \n Sagarna, R.\n\n\n \n\n\n\n Ph.D. Thesis, 2007.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@phdthesis{\n title = {An Optimization Approach for Software Test Data Generation: Applications of Estimation of Distribution Algorithms and Scatter Search},\n type = {phdthesis},\n year = {2007},\n institution = {University of the Basque Country},\n id = {71dd1400-4182-3bf1-a29b-a1ac538157e6},\n created = {2021-11-12T08:30:48.648Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:48.648Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {phdthesis},\n private_publication = {false},\n bibtype = {phdthesis},\n author = {Sagarna, Ramón}\n}
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\n \n\n \n \n \n \n \n A review of feature selection techniques in bioinformatics.\n \n \n \n\n\n \n Saeys, Y.; Inza, I.; and Larrañaga, P.\n\n\n \n\n\n\n Bioinformatics, 23(19): 2507-2517. 2007.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {A review of feature selection techniques in bioinformatics},\n type = {article},\n year = {2007},\n keywords = {isg_ehu,isg_jcr},\n pages = {2507-2517},\n volume = {23},\n id = {7b9d01e6-559e-3188-bdc9-0f89838547c1},\n created = {2021-11-12T08:30:49.582Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:49.582Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Saeys, Yvan and Inza, Iñaki and Larrañaga, Pedro},\n journal = {Bioinformatics},\n number = {19}\n}
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\n \n\n \n \n \n \n \n Aprendizaje exacto de redes Bayesianas en algoritmos de estimación de distribuciones.\n \n \n \n\n\n \n Echegoyen, C.; Santana, R.; and Lozano, J., A.\n\n\n \n\n\n\n In Alba, E.; Chicano, F.; Herrera, F.; Luna, F.; Luque, G.; and Nebro, A., J., editor(s), Actas de las Jornadas de Algoritmos Evolutivos y Metaheur\\'\\isticas (JAEM I), pages 277-284, 2007. Thomson\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Aprendizaje exacto de redes Bayesianas en algoritmos de estimación de distribuciones},\n type = {inproceedings},\n year = {2007},\n keywords = {isg_ehu},\n pages = {277-284},\n publisher = {Thomson},\n id = {a5cf3d21-6f6a-3535-a2ef-9083954c9670},\n created = {2021-11-12T08:30:58.816Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:58.816Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Echegoyen, C and Santana, R and Lozano, J A},\n editor = {Alba, E and Chicano, F and Herrera, F and Luna, F and Luque, G and Nebro, A J},\n booktitle = {Actas de las Jornadas de Algoritmos Evolutivos y Metaheur\\'\\isticas (JAEM I)}\n}
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\n \n\n \n \n \n \n \n Challenges and open problems in discrete EDAs.\n \n \n \n\n\n \n Santana, R.; Larrañaga, P.; and Lozano, J., A.\n\n\n \n\n\n\n Technical Report Department of Computer Science and Artificial Intelligence, University of the Basque Country, 2007.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@techreport{\n title = {Challenges and open problems in discrete EDAs},\n type = {techreport},\n year = {2007},\n keywords = {isg_ehu,isg_jcr},\n issue = {EHU-KZAA-IK-1/07},\n institution = {Department of Computer Science and Artificial Intelligence, University of the Basque Country},\n id = {10e83eab-b6f1-37e4-be78-e51035e53710},\n created = {2021-11-12T08:31:05.964Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:05.964Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {techreport},\n private_publication = {false},\n abstract = {In this paper, we treat the identification of some of the problems that are relevant for the improvement and development of estimation of distribution algorithms. We present a survey of current challenges where further research must provide answers that extend the potential and applicability of the algorithms. In each case we state the problem and elaborate on the reasons that make it relevant for estimation of distribution algorithms. In some cases current work or possible alternatives for the solution of the problem are discussed.},\n bibtype = {techreport},\n author = {Santana, R and Larrañaga, P and Lozano, J A}\n}
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\n In this paper, we treat the identification of some of the problems that are relevant for the improvement and development of estimation of distribution algorithms. We present a survey of current challenges where further research must provide answers that extend the potential and applicability of the algorithms. In each case we state the problem and elaborate on the reasons that make it relevant for estimation of distribution algorithms. In some cases current work or possible alternatives for the solution of the problem are discussed.\n
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\n \n\n \n \n \n \n \n Exact Bayesian network learning in estimation of distribution algorithms.\n \n \n \n\n\n \n Echegoyen, C.; Lozano, J., A.; Santana, R.; and Larrañaga, P.\n\n\n \n\n\n\n In Proceedings of the 2007 Congress on Evolutionary Computation CEC-2007, pages 1051-1058, 2007. IEEE Press\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Exact Bayesian network learning in estimation of distribution algorithms},\n type = {inproceedings},\n year = {2007},\n keywords = {isg_ehu},\n pages = {1051-1058},\n publisher = {IEEE Press},\n id = {d7723508-9f95-357b-a7aa-00de2670e48d},\n created = {2021-11-12T08:31:10.080Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:10.080Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {This paper introduces exact learning of Bayesian networks in estimation of distribution algorithms. The estimation of Bayesian network algorithm (EBNA) is used to analyze the impact of learning the optimal (exact) structure in the search. By applying recently introduced methods that allow learning optimal Bayesian networks, we investigate two important issues in EDAs. First, we analyze the question of whether learning more accurate (exact) models of the dependencies implies a better performance of EDAs. Second, we are able to study the way in which the problem structure is translated into the probabilistic model when exact learning is accomplished.},\n bibtype = {inproceedings},\n author = {Echegoyen, C and Lozano, J A and Santana, R and Larrañaga, P},\n booktitle = {Proceedings of the 2007 Congress on Evolutionary Computation CEC-2007}\n}
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\n This paper introduces exact learning of Bayesian networks in estimation of distribution algorithms. The estimation of Bayesian network algorithm (EBNA) is used to analyze the impact of learning the optimal (exact) structure in the search. By applying recently introduced methods that allow learning optimal Bayesian networks, we investigate two important issues in EDAs. First, we analyze the question of whether learning more accurate (exact) models of the dependencies implies a better performance of EDAs. Second, we are able to study the way in which the problem structure is translated into the probabilistic model when exact learning is accomplished.\n
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\n \n\n \n \n \n \n \n A parallel framework for loopy belief propagation.\n \n \n \n\n\n \n Mendiburu, A.; Santana, R.; Lozano, J., A.; and Bengoetxea, E.\n\n\n \n\n\n\n In Thierens, D., editor(s), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pages 2843-2850, 2007. ACM\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {A parallel framework for loopy belief propagation},\n type = {inproceedings},\n year = {2007},\n keywords = {isg_ehu},\n pages = {2843-2850},\n publisher = {ACM},\n id = {3a5c64fe-7492-3c2e-96e2-c8851de31b3c},\n created = {2021-11-12T08:31:11.754Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:11.754Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Mendiburu, Alexander and Santana, Roberto and Lozano, José A and Bengoetxea, Endika},\n editor = {Thierens, Dirk},\n booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)}\n}
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\n \n\n \n \n \n \n \n Learning Bayesian classifiers from positive and unlabeled examples.\n \n \n \n\n\n \n Calvo, B.; Larrañaga, P.; and Lozano, J., A.\n\n\n \n\n\n\n Pattern Recognition Letters, 28(16): 2375-2384. 2007.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Learning Bayesian classifiers from positive and unlabeled examples},\n type = {article},\n year = {2007},\n keywords = {isg_ehu,isg_jcr},\n pages = {2375-2384},\n volume = {28},\n publisher = {Elsevier},\n id = {52259600-daf6-3698-bfb3-45ad6d302295},\n created = {2021-11-12T08:31:17.180Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:17.180Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Calvo, Borja and Larrañaga, Pedro and Lozano, José A},\n journal = {Pattern Recognition Letters},\n number = {16}\n}
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\n \n\n \n \n \n \n \n Algoritmos de Estimación de Distribuciones Aplicados a Problemas Combinatorios en Modelos Gráficos Probabilísticos.\n \n \n \n\n\n \n Romero Asturiano, T.\n\n\n \n\n\n\n Ph.D. Thesis, 2007.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@phdthesis{\n title = {Algoritmos de Estimación de Distribuciones Aplicados a Problemas Combinatorios en Modelos Gráficos Probabilísticos},\n type = {phdthesis},\n year = {2007},\n institution = {University of the Basque Country},\n id = {bc34af56-5d48-3595-8aff-96d821b84c4f},\n created = {2021-11-12T08:31:25.705Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:25.705Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {phdthesis},\n private_publication = {false},\n bibtype = {phdthesis},\n author = {Romero Asturiano, Txomin}\n}
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\n \n\n \n \n \n \n \n A Partially Supervised Classification Approach to Dominant and Recessive Human Disease Gene Prediction.\n \n \n \n\n\n \n Calvo, B.; López-Bigas, N.; Furney, S., J.; Larrañaga, P.; and Lozano, J., A.\n\n\n \n\n\n\n Computer Methods and Programs in Biomedicine, 85: 229-237. 2007.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@article{\n title = {A Partially Supervised Classification Approach to Dominant and Recessive Human Disease Gene Prediction},\n type = {article},\n year = {2007},\n keywords = {positive.unlabelled.learning},\n pages = {229-237},\n volume = {85},\n id = {de475b0a-e87e-3a35-80ed-9bf0d8030c91},\n created = {2021-11-12T08:31:30.106Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:30.106Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Calvo, Borja and López-Bigas, Núria and Furney, Simon J and Larrañaga, Pedro and Lozano, Jose A},\n journal = {Computer Methods and Programs in Biomedicine}\n}
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\n \n\n \n \n \n \n \n Combining Bayesian classifiers and estimation of distribution algorithms for optimization in continuous domains.\n \n \n \n\n\n \n Miquélez, T.; Bengoetxea, E.; Mendiburu, A.; and Larrañaga, P.\n\n\n \n\n\n\n Connection Science, 19(4): 297-319. 5 2007.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Combining Bayesian classifiers and estimation of distribution algorithms for optimization in continuous domains},\n type = {article},\n year = {2007},\n pages = {297-319},\n volume = {19},\n month = {5},\n id = {da2dd3a2-0884-3925-b31f-1db75f880881},\n created = {2021-11-12T08:31:30.421Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:30.421Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Miquélez, Teresa and Bengoetxea, Endika and Mendiburu, Alexander and Larrañaga, Pedro},\n journal = {Connection Science},\n number = {4}\n}
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\n \n\n \n \n \n \n \n Application of Micro-Genetic Algorithm for Task Based Computing.\n \n \n \n\n\n \n Davidyuk, O.; Selek, I.; Ceberio, J.; and Riekki, J.\n\n\n \n\n\n\n In Intelligent Pervasive Computing, 2007. IPC. The 2007 International Conference on, pages 140-145, 2007. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@inproceedings{\n title = {Application of Micro-Genetic Algorithm for Task Based Computing},\n type = {inproceedings},\n year = {2007},\n keywords = {isg_ehu},\n pages = {140-145},\n id = {5d5013e0-533e-3551-9fa1-b9d54eff21ae},\n created = {2021-11-12T08:31:35.055Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:35.055Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Davidyuk, O and Selek, I and Ceberio, J and Riekki, J},\n booktitle = {Intelligent Pervasive Computing, 2007. IPC. The 2007 International Conference on}\n}
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\n \n\n \n \n \n \n \n Algoritmos de Estimación de Distribuciones para el problema de la determinación de la cadena lateral de una prote\\'\\ina.\n \n \n \n\n\n \n Santana, R.; Lozano, J., A.; and Larrañaga, P.\n\n\n \n\n\n\n In Memorias del V Congreso Español de Algoritmos Evolutivos y Bioinspirados, pages 663-670, 2007. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@inproceedings{\n title = {Algoritmos de Estimación de Distribuciones para el problema de la determinación de la cadena lateral de una prote\\'\\ina},\n type = {inproceedings},\n year = {2007},\n keywords = {isg_ehu},\n pages = {663-670},\n id = {5c76e61d-09b0-385f-a502-9de0bda01b62},\n created = {2021-11-12T08:31:49.692Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:49.692Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {Este trabajo analiza diferentes mejoras a una aplicación precedente de los algoritmos de estimación de distribuciones (EDA) al problema de la determinación de la cadena lateral de una prote\\'\\ina. EDAs simples como el UMDA han permitido la obtención de cadenas laterales con menor valor de energ\\'\\ia que las obtenidas con otros métodos para diferentes secuencias. Sin embargo para algunas secuencias, los resultados obtenidos con el UMDA no mejoran aquellos obtenidos por otros métodos. Por lo tanto, un problema de interés consiste en el estudio de métodos para aumentar la eficacia de los EDAs en la obtención de cadenas laterales de prote\\'\\inas. Presentamos dos posibles alternativas utilizadas con este propósito: el uso de algoritmos de optimización local y el aprendizaje de modelos probabil\\'\\iticos que tienen en cuenta las interacciones entre las variables del problema. Los algoritmos introducidos son evaluados en un conjunto de instanci as dif\\'\\iles. Los resultados obtenidos en este conjunto son superiores a los alcanzados con algoritmos de optimización basados en inferencia},\n bibtype = {inproceedings},\n author = {Santana, R and Lozano, J A and Larrañaga, P},\n booktitle = {Memorias del V Congreso Español de Algoritmos Evolutivos y Bioinspirados}\n}
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\n Este trabajo analiza diferentes mejoras a una aplicación precedente de los algoritmos de estimación de distribuciones (EDA) al problema de la determinación de la cadena lateral de una prote\\'\\ina. EDAs simples como el UMDA han permitido la obtención de cadenas laterales con menor valor de energ\\'\\ia que las obtenidas con otros métodos para diferentes secuencias. Sin embargo para algunas secuencias, los resultados obtenidos con el UMDA no mejoran aquellos obtenidos por otros métodos. Por lo tanto, un problema de interés consiste en el estudio de métodos para aumentar la eficacia de los EDAs en la obtención de cadenas laterales de prote\\'\\inas. Presentamos dos posibles alternativas utilizadas con este propósito: el uso de algoritmos de optimización local y el aprendizaje de modelos probabil\\'\\iticos que tienen en cuenta las interacciones entre las variables del problema. Los algoritmos introducidos son evaluados en un conjunto de instanci as dif\\'\\iles. Los resultados obtenidos en este conjunto son superiores a los alcanzados con algoritmos de optimización basados en inferencia\n
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\n \n\n \n \n \n \n \n Side Chain Placement Using Estimation of Distribution Algorithms.\n \n \n \n\n\n \n Santana, R.; Larrañaga, P.; and Lozano, J., A.\n\n\n \n\n\n\n Artificial Intelligence in Medicine, 39(1): 49-63. 2007.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Side Chain Placement Using Estimation of Distribution Algorithms},\n type = {article},\n year = {2007},\n keywords = {isg_ehu,isg_jcr},\n pages = {49-63},\n volume = {39},\n id = {48654142-dcf6-39e8-9fdf-1de82d6af9b3},\n created = {2021-11-12T08:32:00.915Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:00.915Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {Objective This paper presents an algorithm for the solution of the side chain placement problem. Methods and materials The algorithm combines the application of the Goldstein elimination criterion with the univariate marginal distribution algorithm (UMDA), which stochastically searches the space of possible solutions. The suitability of the algorithm to address the problem is investigated using a set of proteins. Results For a number of difficult instances where inference algorithms do not converge, it has been shown that UMDA is able to find better structures. Conclusions The results obtained show that the algorithm can achieve better structures than those obtained with other state-of-the-art methods like inference-based techniques. Additionally, a theoretical and empirical analysis of the computational cost of the algorithm introduced has been presented.},\n bibtype = {article},\n author = {Santana, R and Larrañaga, P and Lozano, J A},\n journal = {Artificial Intelligence in Medicine},\n number = {1}\n}
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\n Objective This paper presents an algorithm for the solution of the side chain placement problem. Methods and materials The algorithm combines the application of the Goldstein elimination criterion with the univariate marginal distribution algorithm (UMDA), which stochastically searches the space of possible solutions. The suitability of the algorithm to address the problem is investigated using a set of proteins. Results For a number of difficult instances where inference algorithms do not converge, it has been shown that UMDA is able to find better structures. Conclusions The results obtained show that the algorithm can achieve better structures than those obtained with other state-of-the-art methods like inference-based techniques. Additionally, a theoretical and empirical analysis of the computational cost of the algorithm introduced has been presented.\n
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\n  \n 2006\n \n \n (12)\n \n \n
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\n \n\n \n \n \n \n \n Implementation and performance evaluation of a parallelization of estimation of bayesian network algorithms.\n \n \n \n\n\n \n Mendiburu, A.; Migue-Alonso, J.; and Lozano, J., A.\n\n\n \n\n\n\n Parallel processing letters, 16(1): 133-148. 2006.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{\n title = {Implementation and performance evaluation of a parallelization of estimation of bayesian network algorithms},\n type = {article},\n year = {2006},\n pages = {133-148},\n volume = {16},\n publisher = {World Scientific Publishing},\n id = {e3dcf755-cfe1-314e-baf0-96b4bdf52c77},\n created = {2021-11-12T08:30:05.923Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:05.923Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n language = {eng},\n private_publication = {false},\n abstract = {This paper presents, discusses and evaluates parallel implementations of a set of algorithms designed for optimization tasks: Estimation of Bayesian Network Algorithms (EBNAs). These algorithms belong to the family of Evolutionary Computation. Two different APIs have been combined: message passing and threads, with the aim of obtaining good performance levels in a wide range of parallel machines. Our approach has been to analyze the most computationally intensive sections of sequential implementations of EBNAs, and then to parallelize those sections using a master-worker design pattern. This way the resulting program exhibits exactly the same behavior of the sequential one, but runs faster. To evaluate our proposal, we have chosen a complex scenario where EBNAs can be applied: Feature Subset Selection (FSS) for supervised classification problems. For the experiments, three computing systems have been tested: two different clusters built from commodity components, and an 8-way multiprocessor. Programs have been executed on the target machines using different combination of message-passing processes and threads. Achieved performance is excellent, with efficiency around 1. These encouraging results do widen the spectrum of problems (and problem sizes) that can be solved, in reasonable times, with EBNAs.},\n bibtype = {article},\n author = {Mendiburu, A and Migue-Alonso, J and Lozano, J A},\n journal = {Parallel processing letters},\n number = {1}\n}
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\n This paper presents, discusses and evaluates parallel implementations of a set of algorithms designed for optimization tasks: Estimation of Bayesian Network Algorithms (EBNAs). These algorithms belong to the family of Evolutionary Computation. Two different APIs have been combined: message passing and threads, with the aim of obtaining good performance levels in a wide range of parallel machines. Our approach has been to analyze the most computationally intensive sections of sequential implementations of EBNAs, and then to parallelize those sections using a master-worker design pattern. This way the resulting program exhibits exactly the same behavior of the sequential one, but runs faster. To evaluate our proposal, we have chosen a complex scenario where EBNAs can be applied: Feature Subset Selection (FSS) for supervised classification problems. For the experiments, three computing systems have been tested: two different clusters built from commodity components, and an 8-way multiprocessor. Programs have been executed on the target machines using different combination of message-passing processes and threads. Achieved performance is excellent, with efficiency around 1. These encouraging results do widen the spectrum of problems (and problem sizes) that can be solved, in reasonable times, with EBNAs.\n
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\n \n\n \n \n \n \n \n Parallel implementation of Estimation of Distribution Algorithms based on probabilistic graphical models. Application to chemical calibration models.\n \n \n \n\n\n \n Mendiburu-Alberro, A.\n\n\n \n\n\n\n Ph.D. Thesis, 2006.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@phdthesis{\n title = {Parallel implementation of Estimation of Distribution Algorithms based on probabilistic graphical models. Application to chemical calibration models},\n type = {phdthesis},\n year = {2006},\n institution = {University of the Basque Country},\n id = {c6b3cfb3-3a65-37b6-b3f6-c74da280c208},\n created = {2021-11-12T08:30:24.264Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:24.264Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {phdthesis},\n private_publication = {false},\n bibtype = {phdthesis},\n author = {Mendiburu-Alberro, Alexander}\n}
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\n \n\n \n \n \n \n \n Mixtures of Kikuchi approximations.\n \n \n \n\n\n \n Santana, R.; Larrañaga, P.; and Lozano, J., A.\n\n\n \n\n\n\n In Fürnkranz, J.; Scheffer, T.; and Spiliopoulou, M., editor(s), Proceedings of the 17th European Conference on Machine Learning: ECML 2006, volume 4212, of Lecture Notes in Artificial Intelligence, pages 365-376, 2006. Springer\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@inproceedings{\n title = {Mixtures of Kikuchi approximations},\n type = {inproceedings},\n year = {2006},\n keywords = {isg_ehu},\n pages = {365-376},\n volume = {4212},\n publisher = {Springer},\n series = {Lecture Notes in Artificial Intelligence},\n id = {ef57231d-9106-3f83-9fc5-568c8497557c},\n created = {2021-11-12T08:30:31.739Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:31.739Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {Mixtures of distributions concern modeling a probability distribution by a weighted sum of other distributions. Kikuchi approximations of probability distributions follow an approach to approximate the free energy of statistical systems. In this paper, we introduce the mixture of Kikuchi approximations as a probability model. We present an algorithm for learning Kikuchi approximations from data based on the expectation-maximization (EM) paradigm. The proposal is tested in the approximation of probability distributions that arise in evolutionary computation.},\n bibtype = {inproceedings},\n author = {Santana, R and Larrañaga, Pedro and Lozano, J A},\n editor = {Fürnkranz, Johannes and Scheffer, Tobias and Spiliopoulou, Myra},\n booktitle = {Proceedings of the 17th European Conference on Machine Learning: ECML 2006}\n}
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\n Mixtures of distributions concern modeling a probability distribution by a weighted sum of other distributions. Kikuchi approximations of probability distributions follow an approach to approximate the free energy of statistical systems. In this paper, we introduce the mixture of Kikuchi approximations as a probability model. We present an algorithm for learning Kikuchi approximations from data based on the expectation-maximization (EM) paradigm. The proposal is tested in the approximation of probability distributions that arise in evolutionary computation.\n
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\n \n\n \n \n \n \n \n Scatter Search in Software Testing, Comparison and Collaboration with Estimation of Distribution Algorithms.\n \n \n \n\n\n \n Sagarna, R.; and Lozano, J., A.\n\n\n \n\n\n\n European Journal of Operational Research, 169(2): 392-412. 2006.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Scatter Search in Software Testing, Comparison and Collaboration with Estimation of Distribution Algorithms},\n type = {article},\n year = {2006},\n keywords = {isg_ehu,isg_jcr},\n pages = {392-412},\n volume = {169},\n id = {5586544a-5661-3eb1-9bb2-798a4ae41404},\n created = {2021-11-12T08:30:45.060Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:45.060Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Sagarna, Ramón and Lozano, Jose A},\n journal = {European Journal of Operational Research},\n number = {2}\n}
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\n \n\n \n \n \n \n \n Discriminative Learning of Bayesian Network Classifiers.\n \n \n \n\n\n \n Santafé, G.; Lozano, J., A.; and Larrañaga, P.\n\n\n \n\n\n\n Inteligencia Artificial, Revista Iberoamericana de Inteligencia Artificial, 10(29): 39-47. 2006.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Discriminative Learning of Bayesian Network Classifiers},\n type = {article},\n year = {2006},\n keywords = {isg_ehu,isg_jcr},\n pages = {39-47},\n volume = {10},\n id = {b3720df5-4907-3505-b40c-805e4a5b126f},\n created = {2021-11-12T08:30:56.659Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:56.659Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Santafé, Guzmán and Lozano, Jose A and Larrañaga, Pedro},\n journal = {Inteligencia Artificial, Revista Iberoamericana de Inteligencia Artificial},\n number = {29}\n}
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\n \n\n \n \n \n \n \n Evaluation of Parallel EDAs to Create Chemical Calibration Models.\n \n \n \n\n\n \n Mendiburu, A.; Miguel-Alonso, J.; and Lozano, J., A.\n\n\n \n\n\n\n In Second International Conference on e-Science and Grid Technologies (e-Science 2006), 4-6 December 2006, Amsterdam, The Netherlands, pages 118, 2006. IEEE Computer Society\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Evaluation of Parallel EDAs to Create Chemical Calibration Models},\n type = {inproceedings},\n year = {2006},\n keywords = {isg_ehu},\n pages = {118},\n publisher = {IEEE Computer Society},\n id = {b270fe70-c188-3f31-ab0e-c14f60d5a3ce},\n created = {2021-11-12T08:31:10.366Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:10.366Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Mendiburu, Alexander and Miguel-Alonso, José and Lozano, José A},\n booktitle = {Second International Conference on e-Science and Grid Technologies (e-Science 2006), 4-6 December 2006, Amsterdam, The Netherlands}\n}
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\n \n\n \n \n \n \n \n Supervised classification with conditional Gaussian networks: Increasing the structure complexity from naive Bayes.\n \n \n \n\n\n \n Pérez, A.; Larrañaga, P.; and undefined Inza I\n\n\n \n\n\n\n International Journal of Approximate Reasoning, 43(1): 1-25. 5 2006.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Supervised classification with conditional Gaussian networks: Increasing the structure complexity from naive Bayes},\n type = {article},\n year = {2006},\n keywords = {bayesian network,conditional gaussian network,filter,k-dependence bayesian classifiers,naive bayes,semi naive bayes,tree augmented naive bayes,wrapper},\n pages = {1-25},\n volume = {43},\n month = {5},\n id = {cb97eb83-4591-347a-8bd5-d331fed23961},\n created = {2021-11-12T08:31:11.226Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:11.226Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {Most of the Bayesian network-based classifiers are usually only able to handle discrete variables. However, most real-world domains involve continuous variables. A common practice to deal with continuous variables is to discretize them, with a subsequent loss of information. This work shows how discrete classifier induction algorithms can be adapted to the conditional Gaussian network paradigm to deal with continuous variables without discretizing them. In addition, three novel classifier induction algorithms and two new propositions about mutual information are introduced. The classifier induction algorithms presented are ordered and grouped according to their structural complexity: naive Bayes, tree augmented naive Bayes, k-dependence Bayesian classifiers and semi naive Bayes. All the classifier induction algorithms are empirically evaluated using predictive accuracy, and they are compared to linear discriminant analysis, as a continuous classic statistical benchmark classifier. Besides, the accuracies for a set of state-of-the-art classifiers are included in order to justify the use of linear discriminant analysis as the benchmark algorithm. In order to understand the behavior of the conditional Gaussian network-based classifiers better, the results include bias-variance decomposition of the expected misclassification rate. The study suggests that semi naive Bayes structure based classifiers and, especially, the novel wrapper condensed semi naive Bayes backward, outperform the behavior of the rest of the presented classifiers. They also obtain quite competitive results compared to the state-of-the-art algorithms included.},\n bibtype = {article},\n author = {Pérez, A and Larrañaga, P and undefined Inza I, undefined},\n journal = {International Journal of Approximate Reasoning},\n number = {1}\n}
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\n Most of the Bayesian network-based classifiers are usually only able to handle discrete variables. However, most real-world domains involve continuous variables. A common practice to deal with continuous variables is to discretize them, with a subsequent loss of information. This work shows how discrete classifier induction algorithms can be adapted to the conditional Gaussian network paradigm to deal with continuous variables without discretizing them. In addition, three novel classifier induction algorithms and two new propositions about mutual information are introduced. The classifier induction algorithms presented are ordered and grouped according to their structural complexity: naive Bayes, tree augmented naive Bayes, k-dependence Bayesian classifiers and semi naive Bayes. All the classifier induction algorithms are empirically evaluated using predictive accuracy, and they are compared to linear discriminant analysis, as a continuous classic statistical benchmark classifier. Besides, the accuracies for a set of state-of-the-art classifiers are included in order to justify the use of linear discriminant analysis as the benchmark algorithm. In order to understand the behavior of the conditional Gaussian network-based classifiers better, the results include bias-variance decomposition of the expected misclassification rate. The study suggests that semi naive Bayes structure based classifiers and, especially, the novel wrapper condensed semi naive Bayes backward, outperform the behavior of the rest of the presented classifiers. They also obtain quite competitive results compared to the state-of-the-art algorithms included.\n
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\n \n\n \n \n \n \n \n Contributions on Theoretical Aspects of Estimation of Distribution Algorithms.\n \n \n \n\n\n \n González, C.\n\n\n \n\n\n\n Ph.D. Thesis, 2006.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@phdthesis{\n title = {Contributions on Theoretical Aspects of Estimation of Distribution Algorithms},\n type = {phdthesis},\n year = {2006},\n institution = {University of the Basque Country},\n id = {559e273a-aa79-34b6-8bc0-d289c31b56e9},\n created = {2021-11-12T08:31:35.596Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:35.596Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {phdthesis},\n private_publication = {false},\n bibtype = {phdthesis},\n author = {González, Cristina}\n}
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\n \n\n \n \n \n \n \n Bayesian Model Averaging of Naive Bayes for Clustering.\n \n \n \n\n\n \n Santafé, G.; Lozano, J., A.; and Larranaga, P.\n\n\n \n\n\n\n IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 36(5): 1149-1161. 5 2006.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Bayesian Model Averaging of Naive Bayes for Clustering},\n type = {article},\n year = {2006},\n pages = {1149-1161},\n volume = {36},\n month = {5},\n id = {53c8815a-a9ec-371d-85dd-981d50ab430e},\n created = {2021-11-12T08:31:44.938Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:44.938Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {This paper considers a Bayesian model-averaging (MA) approach to learn an unsupervised naive Bayes classification model. By using the expectation model-averaging (EMA) algorithm, which is proposed in this paper, a unique naive Bayes model that approximates an MA over selective naive Bayes structures is obtained. This algorithm allows to obtain the parameters for the approximate MA clustering model in the same time complexity needed to learn the maximum-likelihood model with the expectation-maximization algorithm. On the other hand, the proposed method can also be regarded as an approach to an unsupervised feature subset selection due to the fact that the model obtained by the EMA algorithm incorporates information on how dependent every predictive variable is on the cluster variable},\n bibtype = {article},\n author = {Santafé, G and Lozano, J A and Larranaga, P},\n journal = {IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics)},\n number = {5}\n}
\n
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\n This paper considers a Bayesian model-averaging (MA) approach to learn an unsupervised naive Bayes classification model. By using the expectation model-averaging (EMA) algorithm, which is proposed in this paper, a unique naive Bayes model that approximates an MA over selective naive Bayes structures is obtained. This algorithm allows to obtain the parameters for the approximate MA clustering model in the same time complexity needed to learn the maximum-likelihood model with the expectation-maximization algorithm. On the other hand, the proposed method can also be regarded as an approach to an unsupervised feature subset selection due to the fact that the model obtained by the EMA algorithm incorporates information on how dependent every predictive variable is on the cluster variable\n
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\n \n\n \n \n \n \n \n Parallel EDAs to create multivariate calibration models for quantitative chemical applications.\n \n \n \n\n\n \n Mendiburu, A.; Miguel-Alonso, J.; Lozano, J.; Ostra, M.; and Ubide, C.\n\n\n \n\n\n\n Journal of Parallel and Distributed Computing, 66(8): 1002-1013. 5 2006.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Parallel EDAs to create multivariate calibration models for quantitative chemical applications},\n type = {article},\n year = {2006},\n keywords = {artificial neural network,chemical calibration models,estimation of distribution algorithms,evolutionary algorithms,feature extraction and construction,parallel computing,partial least squares regression},\n pages = {1002-1013},\n volume = {66},\n month = {5},\n id = {551040eb-f8d6-3c01-b7f9-e00811f0dafa},\n created = {2021-11-12T08:31:46.505Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:46.505Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Mendiburu, A and Miguel-Alonso, J and Lozano, J and Ostra, M and Ubide, C},\n journal = {Journal of Parallel and Distributed Computing},\n number = {8}\n}
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\n \n\n \n \n \n \n \n Machine learning in bioinformatics.\n \n \n \n\n\n \n Larrañaga, P.; Calvo, B.; Santana, R.; Bielza, C.; Galdiano, J.; Inza, I.; Lozano, J., A.; Armañanzas, R.; Santafé, G.; Pérez, A.; and Others\n\n\n \n\n\n\n Briefings in bioinformatics, 7(1): 86-112. 2006.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Machine learning in bioinformatics},\n type = {article},\n year = {2006},\n keywords = {isg_ehu,isg_jcr},\n pages = {86-112},\n volume = {7},\n publisher = {Oxford Univ Press},\n id = {5c6b9b6f-1cc7-36bd-ac41-f40c3a33a607},\n created = {2021-11-12T08:31:51.230Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:51.230Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Larrañaga, Pedro and Calvo, Borja and Santana, Roberto and Bielza, Concha and Galdiano, Josu and Inza, Iñaki and Lozano, José A and Armañanzas, Rubén and Santafé, Guzmán and Pérez, Aritz and Others, undefined},\n journal = {Briefings in bioinformatics},\n number = {1}\n}
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\n \n\n \n \n \n \n \n \n Advances in Probabilistic Graphical Models for Optimization and Learning Applications in Protein Modelling.\n \n \n \n \n\n\n \n Santana, R.\n\n\n \n\n\n\n Ph.D. Thesis, 2006.\n \n\n\n\n
\n\n\n\n \n \n \"AdvancesWebsite\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@phdthesis{\n title = {Advances in Probabilistic Graphical Models for Optimization and Learning Applications in Protein Modelling},\n type = {phdthesis},\n year = {2006},\n websites = {https://github.com/isg-ehu/PhD-Dissertations/raw/master/2006_phd_roberto_santana.pdf},\n institution = {University of the Basque Country},\n id = {3cef1bb1-1d82-302d-b4a6-2dd263450b60},\n created = {2021-11-12T08:32:16.724Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:16.724Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {phdthesis},\n private_publication = {false},\n bibtype = {phdthesis},\n author = {Santana, Roberto}\n}
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\n  \n 2005\n \n \n (16)\n \n \n
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\n \n\n \n \n \n \n \n Minería de Datos: Técnicas y Aplicaciones.\n \n \n \n\n\n \n Armañanzas, R.; Calvo, B.; Inza, I.; Larrañaga, P.; Bernales, I.; Fullaondo, A.; and Zubiaga, A., M.\n\n\n \n\n\n\n pages 107-135. Gámez, J., A.; Varea, I., G.; and Orallo, J., H., editor(s). Ediciones Departamento de Informática de la Universidad de Castilla La Mancha, 2005.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inbook{\n type = {inbook},\n year = {2005},\n pages = {107-135},\n publisher = {Ediciones Departamento de Informática de la Universidad de Castilla La Mancha},\n chapter = {Minería de Datos: Técnicas y Aplicaciones},\n id = {1642c499-121f-3aac-b2e6-fab1bed5d6ef},\n created = {2021-11-12T08:30:03.430Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:03.430Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {incollection},\n private_publication = {false},\n bibtype = {inbook},\n author = {Armañanzas, Rubén and Calvo, Borja and Inza, Iñaki and Larrañaga, Pedro and Bernales, Irantzu and Fullaondo, Asier and Zubiaga, Ana M},\n editor = {Gámez, José A and Varea, Ismael García and Orallo, José Hernández}\n}
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\n \n\n \n \n \n \n \n I Workshop sobre Reconocimiento de Formas y Análisis de Imágenes.\n \n \n \n\n\n \n Calvo, B.; Larrañaga, P.; and Lozano, J., A.\n\n\n \n\n\n\n pages 3-12. de la Blanca Capilla, N., P.; and Bañón, F., P., editor(s). Thomson, 2005.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inbook{\n type = {inbook},\n year = {2005},\n keywords = {positive.unlabelled.learning},\n pages = {3-12},\n publisher = {Thomson},\n chapter = {I Workshop sobre Reconocimiento de Formas y Análisis de Imágenes},\n id = {93efbb03-ec75-3473-98bd-9dfa2c66f0b4},\n created = {2021-11-12T08:30:04.244Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:04.244Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {incollection},\n private_publication = {false},\n bibtype = {inbook},\n author = {Calvo, Borja and Larrañaga, Pedro and Lozano, Jose Antonio},\n editor = {de la Blanca Capilla, Nicolás Pérez and Bañón, Filiberto Plá}\n}
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\n \n\n \n \n \n \n \n Proceedings of the Sixth Spanish Symposium on Technology Transfer from Artificial Intelligence.\n \n \n \n\n\n \n Armañanzas, R.; Calvo, B.; Inza, I.; Larrañaga, P.; Bernales, I.; Fullaondo, A.; and Zubiaga, A., M.\n\n\n \n\n\n\n pages 63-70. Thomson, 2005.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@inbook{\n type = {inbook},\n year = {2005},\n pages = {63-70},\n publisher = {Thomson},\n chapter = {Proceedings of the Sixth Spanish Symposium on Technology Transfer from Artificial Intelligence},\n id = {063ccd90-88dd-31c7-87e2-95b1c7b40a37},\n created = {2021-11-12T08:30:04.542Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:04.542Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {incollection},\n private_publication = {false},\n bibtype = {inbook},\n author = {Armañanzas, Rubén and Calvo, Borja and Inza, Iñaki and Larrañaga, Pedro and Bernales, Irantzu and Fullaondo, Asier and Zubiaga, Ana María}\n}
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\n \n\n \n \n \n \n \n On the Performance of Estimation of Distribution Algorithms Applied to Software Testing.\n \n \n \n\n\n \n Sagarna, R.; and Lozano, J., A.\n\n\n \n\n\n\n Applied Artificial Intelligence, 19(5): 457-489. 2005.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {On the Performance of Estimation of Distribution Algorithms Applied to Software Testing},\n type = {article},\n year = {2005},\n keywords = {isg_ehu,isg_jcr},\n pages = {457-489},\n volume = {19},\n id = {7359a089-4a66-3044-a0a3-4eceac815974},\n created = {2021-11-12T08:30:07.340Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:07.340Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Sagarna, Ramón and Lozano, Jose A},\n journal = {Applied Artificial Intelligence},\n number = {5}\n}
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\n \n\n \n \n \n \n \n Parallel and Multi-Objective EDAs to Create Multivariate Calibration Models for Quantitative Chemical Applications.\n \n \n \n\n\n \n Mendiburu, A.; Miguel-Alonso, J.; Lozano, J., A.; Ostra, M.; and Ubide, C.\n\n\n \n\n\n\n In 34th International Conference on Parallel Processing Workshops (ICPP 2005 Workshops), 14-17 June 2005, Oslo, Norway, pages 596-603, 2005. IEEE Computer Society\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Parallel and Multi-Objective EDAs to Create Multivariate Calibration Models for Quantitative Chemical Applications},\n type = {inproceedings},\n year = {2005},\n keywords = {isg_ehu},\n pages = {596-603},\n publisher = {IEEE Computer Society},\n id = {fdc6a67e-c0e8-35db-8ee6-a48f0722031e},\n created = {2021-11-12T08:30:10.630Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:10.630Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n bibtype = {inproceedings},\n author = {Mendiburu, Alexander and Miguel-Alonso, José and Lozano, José A and Ostra, M and Ubide, C},\n booktitle = {34th International Conference on Parallel Processing Workshops (ICPP 2005 Workshops), 14-17 June 2005, Oslo, Norway}\n}
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\n \n\n \n \n \n \n \n Special issue on PGMC.\n \n \n \n\n\n \n Larrañaga, P.; Lozano, J., A.; Peña, J., M.; and Inza, I.\n\n\n \n\n\n\n Machine Learning, 59(3): 211-212. 2005.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Special issue on PGMC},\n type = {article},\n year = {2005},\n keywords = {isg_ehu,isg_jcr},\n pages = {211-212},\n volume = {59},\n id = {55356256-8e19-3fc0-a826-2b3bba270c4c},\n created = {2021-11-12T08:30:15.688Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:15.688Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Larrañaga, Pedro and Lozano, Jose A and Peña, Jose M and Inza, Iñaki},\n journal = {Machine Learning},\n number = {3}\n}
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\n \n\n \n \n \n \n \n Properties of Kikuchi approximations constructed from clique based decompositions.\n \n \n \n\n\n \n Santana, R.; Larrañaga, P.; and Lozano, J., A.\n\n\n \n\n\n\n Technical Report Department of Computer Science and Artificial Intelligence, University of the Basque Country, 2005.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@techreport{\n title = {Properties of Kikuchi approximations constructed from clique based decompositions},\n type = {techreport},\n year = {2005},\n keywords = {isg_ehu,isg_jcr},\n issue = {EHU-KZAA-IK-2/05},\n institution = {Department of Computer Science and Artificial Intelligence, University of the Basque Country},\n id = {db3a51a1-c2f7-368d-ac2f-21bb17c1214e},\n created = {2021-11-12T08:30:40.080Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:40.080Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {techreport},\n private_publication = {false},\n abstract = {Kikuchi approximations constructed from clique-based decompositions can be used to calculate suitable approximations of probability distributions. They can be applied in domains such as probabilistic modeling, supervised and unsupervised classi\fcation, and evolutionary algorithms. This paper introduces a number of properties of these approximations. Pairwise and local Markov properties of the Kikuchi approximations are proved. We prove that, even if the global Markov property is not satisfied in the general case, it is possible to decompose the Kikuchi approximation in the product of local Kikuchi approximations defined on a decomposition of the graph. Partial Kikuchi approximations are introduced. Additionally, the paper clarifies the place of clique-based decompositions in relation to other techniques inspired by methods from statistical physics, and discusses the application of the results introduced in the paper for the conception of Kikuchi approxim ation learning algorithms.},\n bibtype = {techreport},\n author = {Santana, R and Larrañaga, P and Lozano, J A}\n}
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\n Kikuchi approximations constructed from clique-based decompositions can be used to calculate suitable approximations of probability distributions. They can be applied in domains such as probabilistic modeling, supervised and unsupervised classi\fcation, and evolutionary algorithms. This paper introduces a number of properties of these approximations. Pairwise and local Markov properties of the Kikuchi approximations are proved. We prove that, even if the global Markov property is not satisfied in the general case, it is possible to decompose the Kikuchi approximation in the product of local Kikuchi approximations defined on a decomposition of the graph. Partial Kikuchi approximations are introduced. Additionally, the paper clarifies the place of clique-based decompositions in relation to other techniques inspired by methods from statistical physics, and discusses the application of the results introduced in the paper for the conception of Kikuchi approxim ation learning algorithms.\n
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\n \n\n \n \n \n \n \n Parallel Implementation of EDAs based on Probabilistic Graphical Models.\n \n \n \n\n\n \n Mendiburu, A.; Lozano, J.; and Miguel-Alonso, J.\n\n\n \n\n\n\n IEEE Transactions on Evolutionary Computation, 9(4): 406-423. 2005.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Parallel Implementation of EDAs based on Probabilistic Graphical Models},\n type = {article},\n year = {2005},\n pages = {406-423},\n volume = {9},\n id = {022c9a77-5116-362f-9c10-89ee15186a8f},\n created = {2021-11-12T08:30:49.303Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:49.303Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {This paper proposes new parallel versions of some estimation of distribution algorithms (EDAs). Focus is on maintenance of the behavior of sequential EDAs that use probabilistic graphical models (Bayesian networks and Gaussian networks), implementing a master&8211;slave workload distribution for the most computationally intensive phases: learning the probability distribution and, in one algorithm, &8220;sampling and evaluation of individuals.&8221; In discrete domains, we explain the parallelization of EBNA BICand EBNA PCalgorithms, while in continuous domains, the selected algorithms are EGNA BICand EGNA EE. Implementation has been done using two APIs: message passing interface and POSIX threads. The parallel programs can run efficiently on a range of target parallel computers. Experiments to evaluate the programs in terms of speed up and efficiency have been carried out on a cluster of multiprocessors. Compared with the sequential versions, they show reasonable gains in terms of speed.},\n bibtype = {article},\n author = {Mendiburu, A and Lozano, J and Miguel-Alonso, J},\n journal = {IEEE Transactions on Evolutionary Computation},\n number = {4}\n}
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\n This paper proposes new parallel versions of some estimation of distribution algorithms (EDAs). Focus is on maintenance of the behavior of sequential EDAs that use probabilistic graphical models (Bayesian networks and Gaussian networks), implementing a master&8211;slave workload distribution for the most computationally intensive phases: learning the probability distribution and, in one algorithm, &8220;sampling and evaluation of individuals.&8221; In discrete domains, we explain the parallelization of EBNA BICand EBNA PCalgorithms, while in continuous domains, the selected algorithms are EGNA BICand EGNA EE. Implementation has been done using two APIs: message passing interface and POSIX threads. The parallel programs can run efficiently on a range of target parallel computers. Experiments to evaluate the programs in terms of speed up and efficiency have been carried out on a cluster of multiprocessors. Compared with the sequential versions, they show reasonable gains in terms of speed.\n
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\n \n\n \n \n \n \n \n Estimation of distribution algorithms with Kikuchi approximations.\n \n \n \n\n\n \n Santana, R.\n\n\n \n\n\n\n Evolutionary Computation, 13(1): 67-97. 2005.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Estimation of distribution algorithms with Kikuchi approximations},\n type = {article},\n year = {2005},\n keywords = {isg_ehu,isg_jcr},\n pages = {67-97},\n volume = {13},\n id = {007188ac-fe4c-3346-93bb-038b98fa367e},\n created = {2021-11-12T08:30:50.406Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:50.406Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {The question of finding feasible ways for estimating probability distributions is one of the main challenges for Estimation of Distribution Algorithms (EDAs). To estimate the distribution of the selected solutions, EDAs use factorizations constructed according to graphical models. The class of factorizations that can be obtained from these probability models is highly constrained. Expanding the class of factorizations that could be employed for probability approximation is a necessary step for the conception of more robust EDAs. In this paper we introduce a method for learning a more general class of probability factorizations. The method combines a reformulation of a probability approximation procedure known in statistical physics as the Kikuchi approximation of energy, with a novel approach for finding graph decompositions. We present the Markov Network Estimation of Distribution Algorithm (MN-EDA), an EDA that uses Kikuchi approximations to es timate t he distribution, and Gibbs Sampling (GS) to generate new points. A systematic empirical evaluation of MN-EDA is done in comparison with different Bayesian network based EDAs. From our experiments we conclude that the algorithm can outperform other EDAs that use traditional methods of probability approximation in the optimization of functions with strong interactions among their variables.},\n bibtype = {article},\n author = {Santana, R},\n journal = {Evolutionary Computation},\n number = {1}\n}
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\n The question of finding feasible ways for estimating probability distributions is one of the main challenges for Estimation of Distribution Algorithms (EDAs). To estimate the distribution of the selected solutions, EDAs use factorizations constructed according to graphical models. The class of factorizations that can be obtained from these probability models is highly constrained. Expanding the class of factorizations that could be employed for probability approximation is a necessary step for the conception of more robust EDAs. In this paper we introduce a method for learning a more general class of probability factorizations. The method combines a reformulation of a probability approximation procedure known in statistical physics as the Kikuchi approximation of energy, with a novel approach for finding graph decompositions. We present the Markov Network Estimation of Distribution Algorithm (MN-EDA), an EDA that uses Kikuchi approximations to es timate t he distribution, and Gibbs Sampling (GS) to generate new points. A systematic empirical evaluation of MN-EDA is done in comparison with different Bayesian network based EDAs. From our experiments we conclude that the algorithm can outperform other EDAs that use traditional methods of probability approximation in the optimization of functions with strong interactions among their variables.\n
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\n \n\n \n \n \n \n \n Learning Bayesian Networks from Data with Factorisation and Classification Purposes. Applications in Biomedicine.\n \n \n \n\n\n \n Blanco, R.\n\n\n \n\n\n\n Ph.D. Thesis, 2005.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@phdthesis{\n title = {Learning Bayesian Networks from Data with Factorisation and Classification Purposes. Applications in Biomedicine},\n type = {phdthesis},\n year = {2005},\n institution = {University of the Basque Country},\n id = {c4a05b7e-7750-3fcd-86b8-3de942ac9303},\n created = {2021-11-12T08:31:04.891Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:04.891Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {phdthesis},\n private_publication = {false},\n bibtype = {phdthesis},\n author = {Blanco, Rosa}\n}
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\n \n\n \n \n \n \n \n Protein structure prediction in simplified models with estimation of distribution algorithms.\n \n \n \n\n\n \n Santana, R.; Larrañaga, P.; and Lozano, J., A.\n\n\n \n\n\n\n In Proceedings of the IV Congreso Español sobre Metaheur\\'\\isticas, Algoritmos Evolutivos y Bioinspirados (MAEB-2005), pages 245-252, 2005. Thomson\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Protein structure prediction in simplified models with estimation of distribution algorithms},\n type = {inproceedings},\n year = {2005},\n keywords = {isg_ehu},\n pages = {245-252},\n publisher = {Thomson},\n id = {8061b430-8ba5-3b44-9908-3fe4f40cbc87},\n created = {2021-11-12T08:31:14.072Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:14.072Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {In this paper we discuss the use of probabilistic modeling in the solution of the protein structure prediction problem. Estimation of distribution algorithms (EDAs) based on Markov models are presented as an alternative to other nature-inspired optimization algorithms for the solution of protein simplified models.},\n bibtype = {inproceedings},\n author = {Santana, R and Larrañaga, P and Lozano, J A},\n booktitle = {Proceedings of the IV Congreso Español sobre Metaheur\\'\\isticas, Algoritmos Evolutivos y Bioinspirados (MAEB-2005)}\n}
\n
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\n In this paper we discuss the use of probabilistic modeling in the solution of the protein structure prediction problem. Estimation of distribution algorithms (EDAs) based on Markov models are presented as an alternative to other nature-inspired optimization algorithms for the solution of protein simplified models.\n
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\n \n\n \n \n \n \n \n Globally Multimodal Problem Optimization Via an Estimation of Distribution Algorithm Based on Unsupervised Learning of Bayesian Networks.\n \n \n \n\n\n \n Peña, J., M.; Lozano, J., A.; and Larrañaga, P.\n\n\n \n\n\n\n Evolutionary Computation, 13(1): 43-66. 2005.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Globally Multimodal Problem Optimization Via an Estimation of Distribution Algorithm Based on Unsupervised Learning of Bayesian Networks},\n type = {article},\n year = {2005},\n keywords = {isg_ehu,isg_jcr},\n pages = {43-66},\n volume = {13},\n id = {2bb824fd-b476-3ccc-a325-8acf1dc723cf},\n created = {2021-11-12T08:31:16.039Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:16.039Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Peña, Jose M and Lozano, Jose A and Larrañaga, Pedro},\n journal = {Evolutionary Computation},\n number = {1}\n}
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\n \n\n \n \n \n \n \n Interactions and dependencies in estimation of distribution algorithms.\n \n \n \n\n\n \n Santana, R.; Larrañaga, P.; and Lozano, J., A.\n\n\n \n\n\n\n In Proceedings of the 2005 Congress on Evolutionary Computation CEC-2005, pages 1418-1425, 2005. IEEE Press\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Interactions and dependencies in estimation of distribution algorithms},\n type = {inproceedings},\n year = {2005},\n keywords = {isg_ehu},\n pages = {1418-1425},\n publisher = {IEEE Press},\n id = {c0b3b8ea-7944-364c-b7a5-3c347755108f},\n created = {2021-11-12T08:31:28.665Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:28.665Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {In this paper, we investigate two issues related to probabilistic modeling in estimation of distribution algorithms (EDAs). First, we analyze the effect of selection in the arousal of probability dependencies in EDAs for random functions. We show that, for these functions, independence relationships not represented by the function structure are likely to appear in the probability model. Second, we propose an approach to approximate probability distributions in EDAs using a subset of the dependencies that exist in the data. An EDA that employs only malign interactions is introduced. Preliminary experiments presented show how the probability approximations based solely on malign interactions, can be applied to EDAs.},\n bibtype = {inproceedings},\n author = {Santana, R and Larrañaga, P and Lozano, J A},\n booktitle = {Proceedings of the 2005 Congress on Evolutionary Computation CEC-2005}\n}
\n
\n\n\n
\n In this paper, we investigate two issues related to probabilistic modeling in estimation of distribution algorithms (EDAs). First, we analyze the effect of selection in the arousal of probability dependencies in EDAs for random functions. We show that, for these functions, independence relationships not represented by the function structure are likely to appear in the probability model. Second, we propose an approach to approximate probability distributions in EDAs using a subset of the dependencies that exist in the data. An EDA that employs only malign interactions is introduced. Preliminary experiments presented show how the probability approximations based solely on malign interactions, can be applied to EDAs.\n
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\n \n\n \n \n \n \n \n Aprendizaje y muestreo de la aproximación Kikuchi.\n \n \n \n\n\n \n Santana, R.; Larrañaga, P.; and Lozano, J., A.\n\n\n \n\n\n\n In Proceedings of the III Taller Nacional de Miner\\'\\ia de Datos y Aprendizaje (TAMIDA-2005), pages 97-105, 2005. Thomson\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
\n 
@inproceedings{\n title = {Aprendizaje y muestreo de la aproximación Kikuchi},\n type = {inproceedings},\n year = {2005},\n keywords = {isg_ehu},\n pages = {97-105},\n publisher = {Thomson},\n id = {5f0cb197-e039-304e-94d0-6555224bbdc1},\n created = {2021-11-12T08:31:37.333Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:37.333Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {En este trabajo se presentan un algoritmo para el aprendizaje de la aproximación Kikuchi a partir de datos as\\'\\i como un método de muestreo de la referida aproximación. Se discuten resultados preliminares de la evaluación del algoritmo en el aprendizaje en datos generados a partir de una instancia del modelo Ising de ferromagnetismo. Los resultados obtenidos indican que la aproximación Kikuchi es capaz de representar de manera factible las dependencias existentes en los datos entre las distintas variables.},\n bibtype = {inproceedings},\n author = {Santana, R and Larrañaga, P and Lozano, J A},\n booktitle = {Proceedings of the III Taller Nacional de Miner\\'\\ia de Datos y Aprendizaje (TAMIDA-2005)}\n}
\n
\n\n\n
\n En este trabajo se presentan un algoritmo para el aprendizaje de la aproximación Kikuchi a partir de datos as\\'\\i como un método de muestreo de la referida aproximación. Se discuten resultados preliminares de la evaluación del algoritmo en el aprendizaje en datos generados a partir de una instancia del modelo Ising de ferromagnetismo. Los resultados obtenidos indican que la aproximación Kikuchi es capaz de representar de manera factible las dependencias existentes en los datos entre las distintas variables.\n
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\n \n\n \n \n \n \n \n Feature selection in Bayesian classifiers for the prognosis of survival of cirrhotic patients treated with TIPS.\n \n \n \n\n\n \n Blanco, R.; Inza, I.; Merino, M.; Quiroga, J.; and Larrañaga, P.\n\n\n \n\n\n\n Journal of Biomedical Informatics, 5(38). 2005.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Feature selection in Bayesian classifiers for the prognosis of survival of cirrhotic patients treated with TIPS},\n type = {article},\n year = {2005},\n keywords = {isg_ehu,isg_jcr},\n volume = {5},\n id = {eba2fd9b-c0a1-3519-86a3-87e3904ce1e3},\n created = {2021-11-12T08:31:47.076Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:47.076Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Blanco, Rosa and Inza, Iñaki and Merino, Marisa and Quiroga, Jorge and Larrañaga, Pedro},\n journal = {Journal of Biomedical Informatics},\n number = {38}\n}
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\n \n\n \n \n \n \n \n A novel in-silico approach for QSAR Studies of Anabolic and Androgenic Activities in the 17-hydroxy-5-androstane Steroid Family.\n \n \n \n\n\n \n Alvarez-Ginarte, Y., M.; Crespo, R.; Montero-Cabrera, L., A.; Ruiz-Garcia, J., A.; Ponce, Y., M.; Santana, R.; Pardillo-Fontdevila, E.; and Alonso-Becerra, E.\n\n\n \n\n\n\n QSAR & Combinatorial Science, 24: 218-226. 2005.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {A novel in-silico approach for QSAR Studies of Anabolic and Androgenic Activities in the 17-hydroxy-5-androstane Steroid Family},\n type = {article},\n year = {2005},\n keywords = {isg_ehu,isg_jcr},\n pages = {218-226},\n volume = {24},\n id = {13851d77-947e-35db-bee9-442e1416aa70},\n created = {2021-11-12T08:32:01.771Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:01.771Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {Predictive Quantitative Structure-Activity Relationship (QSAR) models of anabolic and androgenic activities for the 17β-hydroxy-5α-androstane steroid family were obtained by means of multi-linear regression using quantum and physicochemical molecular descriptors and a genetic algorithm for the selection of the best set of descriptors. The model allows the identification, selection and future design of new steroid molecules with increased anabolic activity. Molecular descriptors included in reported models allow the structural interpretation of the biological process, evidencing the main role of the shape of molecules, hydrophobicity and electronic properties. The model for the anabolic/androgenic ratio (expressed by the weight of the levator ani muscle and ventral prostate in mice) predicts that: a) 2-cyano-17-α-methyl-17-β-acetoxy-5α-androst-2-ene is the most potent anabolic steroid in the group and b) the testosterone-3-cyclopentenyl-enole ter is t he less potent one. The approach described in this paper is an alternative for the discovery and optimization of leading anabolic compounds.},\n bibtype = {article},\n author = {Alvarez-Ginarte, Yoanna M and Crespo, Rachel and Montero-Cabrera, Luis A and Ruiz-Garcia, José A and Ponce, Yovani M and Santana, Roberto and Pardillo-Fontdevila, Eladio and Alonso-Becerra, Esther},\n journal = {QSAR & Combinatorial Science}\n}
\n
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\n Predictive Quantitative Structure-Activity Relationship (QSAR) models of anabolic and androgenic activities for the 17β-hydroxy-5α-androstane steroid family were obtained by means of multi-linear regression using quantum and physicochemical molecular descriptors and a genetic algorithm for the selection of the best set of descriptors. The model allows the identification, selection and future design of new steroid molecules with increased anabolic activity. Molecular descriptors included in reported models allow the structural interpretation of the biological process, evidencing the main role of the shape of molecules, hydrophobicity and electronic properties. The model for the anabolic/androgenic ratio (expressed by the weight of the levator ani muscle and ventral prostate in mice) predicts that: a) 2-cyano-17-α-methyl-17-β-acetoxy-5α-androst-2-ene is the most potent anabolic steroid in the group and b) the testosterone-3-cyclopentenyl-enole ter is t he less potent one. The approach described in this paper is an alternative for the discovery and optimization of leading anabolic compounds.\n
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\n  \n 2004\n \n \n (7)\n \n \n
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\n \n\n \n \n \n \n \n Filter versus wrapper gene selection approaches in DNA microarray domains.\n \n \n \n\n\n \n Inza, I.; Larrañaga, P.; Blanco, R.; and Cerrolaza, A., J.\n\n\n \n\n\n\n Artificial Intelligence in Medicine, 31(2). 2004.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Filter versus wrapper gene selection approaches in DNA microarray domains},\n type = {article},\n year = {2004},\n keywords = {isg_ehu,isg_jcr},\n volume = {31},\n id = {8305621d-c6c9-36d6-a5ca-0f46a0cf157a},\n created = {2021-11-12T08:30:08.635Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:08.635Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Inza, Iñaki and Larrañaga, Pedro and Blanco, Rosa and Cerrolaza, Antonio J},\n journal = {Artificial Intelligence in Medicine},\n number = {2}\n}
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\n \n\n \n \n \n \n \n Unsupervised learning of Bayesian networks via estimation of distribution algorithms: an application to gene expression data clustering.\n \n \n \n\n\n \n Peña, J., M.; Lozano, J., A.; and Larrañaga, P.\n\n\n \n\n\n\n International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 12(1 supp): 63-82. 2004.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Unsupervised learning of Bayesian networks via estimation of distribution algorithms: an application to gene expression data clustering},\n type = {article},\n year = {2004},\n keywords = {isg_ehu,isg_jcr},\n pages = {63-82},\n volume = {12},\n id = {acb15d75-84a9-3751-94de-c7a2f903a085},\n created = {2021-11-12T08:30:35.069Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:35.069Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Peña, Jose M and Lozano, Jose A and Larrañaga, Pedro},\n journal = {International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems},\n number = {1 supp}\n}
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\n \n\n \n \n \n \n \n Evolutionary computations based on bayesian classifiers.\n \n \n \n\n\n \n Miquélez, T.; Bengoetxea, E.; and Larrañaga, P.\n\n\n \n\n\n\n INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS AND COMPUTER SCIENCE, 14: 101-115. 2004.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Evolutionary computations based on bayesian classifiers},\n type = {article},\n year = {2004},\n keywords = {isg_ehu,isg_jcr},\n pages = {101-115},\n volume = {14},\n id = {3def862b-b99b-3a42-a41a-e6de5d352d0b},\n created = {2021-11-12T08:30:57.206Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:57.206Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Miquélez, Teresa and Bengoetxea, Endika and Larrañaga, Pedro},\n journal = {INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS AND COMPUTER SCIENCE}\n}
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\n \n\n \n \n \n \n \n Protein folding in 2-dimensional lattices with estimation of distribution algorithms.\n \n \n \n\n\n \n Santana, R.; Larrañaga, P.; and Lozano, J., A.\n\n\n \n\n\n\n In Proceedings of the First International Symposium on Biological and Medical Data Analysis, volume 3337, of Lecture Notes in Computer Science, pages 388-398, 2004. Springer\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n\n\n\n
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@inproceedings{\n title = {Protein folding in 2-dimensional lattices with estimation of distribution algorithms},\n type = {inproceedings},\n year = {2004},\n keywords = {isg_ehu},\n pages = {388-398},\n volume = {3337},\n publisher = {Springer},\n series = {Lecture Notes in Computer Science},\n id = {4a28d349-cc3d-307c-b6ed-996520fcaea5},\n created = {2021-11-12T08:30:59.370Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:59.370Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {This paper introduces a new type of evolutionary computation algorithm based on probability distributions for the solution of two simplified protein folding models. The relationship of the introduced algorithm with previous evolutionary methods used for protein folding is discussed. A number of experiments for difficult instances of the models under analysis is presented. For the instances considered, the algorithm is shown to outperform previous evolutionary optimization methods.},\n bibtype = {inproceedings},\n author = {Santana, R and Larrañaga, P and Lozano, J A},\n booktitle = {Proceedings of the First International Symposium on Biological and Medical Data Analysis}\n}
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\n\n\n
\n This paper introduces a new type of evolutionary computation algorithm based on probability distributions for the solution of two simplified protein folding models. The relationship of the introduced algorithm with previous evolutionary methods used for protein folding is discussed. A number of experiments for difficult instances of the models under analysis is presented. For the instances considered, the algorithm is shown to outperform previous evolutionary optimization methods.\n
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\n \n\n \n \n \n \n \n Bayesian network multi-classifiers for protein secondary structure prediction.\n \n \n \n\n\n \n Robles, V.; Larrañaga, P.; Peña, J., M.; Menasalvas, E.; Pérez, M., S.; Herves, V.; and Wasilewska, A.\n\n\n \n\n\n\n Artificial Intelligence in Medicine, 31(2): 117-136. 2004.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Bayesian network multi-classifiers for protein secondary structure prediction},\n type = {article},\n year = {2004},\n keywords = {isg_ehu,isg_jcr},\n pages = {117-136},\n volume = {31},\n id = {0ee20dd9-e409-3bbf-a972-fd5602a5049e},\n created = {2021-11-12T08:31:23.822Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:23.822Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Robles, V\\'\\ictor and Larrañaga, Pedro and Peña, Jose M and Menasalvas, Ernestina and Pérez, Mar\\'\\ia S and Herves, Vanessa and Wasilewska, Anita},\n journal = {Artificial Intelligence in Medicine},\n number = {2}\n}
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\n \n\n \n \n \n \n \n Learning Bayesian Networks In The Space Of Orderings With Estimation Of Distribution Algorithms.\n \n \n \n\n\n \n Romero, T.; Larrañaga, P.; and Sierra, B.\n\n\n \n\n\n\n IJPRAI, 18(4): 607-625. 2004.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Learning Bayesian Networks In The Space Of Orderings With Estimation Of Distribution Algorithms},\n type = {article},\n year = {2004},\n keywords = {isg_ehu,isg_jcr},\n pages = {607-625},\n volume = {18},\n id = {e004bf94-0280-31d3-9825-2369ca7e5fbc},\n created = {2021-11-12T08:31:30.759Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:30.759Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Romero, Txomin and Larrañaga, Pedro and Sierra, Basilio},\n journal = {IJPRAI},\n number = {4}\n}
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\n \n\n \n \n \n \n \n Gene selection for cancer classification using wrapper approaches.\n \n \n \n\n\n \n Blanco, R.; Larrañaga, P.; Inza, I.; and Sierra, B.\n\n\n \n\n\n\n International Journal of Pattern Recognition and Artificial Intelligence, 18(8): 1373-1390. 2004.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Gene selection for cancer classification using wrapper approaches},\n type = {article},\n year = {2004},\n keywords = {isg_ehu,isg_jcr},\n pages = {1373-1390},\n volume = {18},\n id = {309a7724-52b6-3cb1-ae52-d8bdd10591c2},\n created = {2021-11-12T08:31:46.199Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:46.199Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Blanco, Rosa and Larrañaga, Pedro and Inza, Iñaki and Sierra, Basilio},\n journal = {International Journal of Pattern Recognition and Artificial Intelligence},\n number = {8}\n}
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\n  \n 2003\n \n \n (3)\n \n \n
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\n \n\n \n \n \n \n \n Analysis of the Univariate Marginal Distribution Algorithm Modeled by Markov Chains.\n \n \n \n\n\n \n Gonzalez, C.; Rodr\\'\\iguez, J., D.; Lozano, J., A.; and Larrañaga, P.\n\n\n \n\n\n\n Lecture Notes in Computer Science, 2686: 510-517. 2003.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Analysis of the Univariate Marginal Distribution Algorithm Modeled by Markov Chains.},\n type = {article},\n year = {2003},\n keywords = {isg_ehu,isg_jcr},\n pages = {510-517},\n volume = {2686},\n id = {7db12ba2-e18b-3ea1-b6cf-26b7e72275a0},\n created = {2021-11-12T08:30:50.953Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:50.953Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Gonzalez, Cristina and Rodr\\'\\iguez, Juan D and Lozano, Jose A and Larrañaga, Pedro},\n journal = {Lecture Notes in Computer Science}\n}
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\n \n\n \n \n \n \n \n Algoritmos de estimación de distribuciones en problemas de optimización combinatoria.\n \n \n \n\n\n \n Larrañaga, P.; Lozano, J., A.; and Mühlenbein, H.\n\n\n \n\n\n\n Revista Iberoamericana de Inteligencia Artificial, 31(3): 155-1566. 2003.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Algoritmos de estimación de distribuciones en problemas de optimización combinatoria},\n type = {article},\n year = {2003},\n keywords = {isg_ehu,isg_jcr},\n pages = {155-1566},\n volume = {31},\n id = {88299f4e-da5d-3d02-9ba2-43ec227f4c45},\n created = {2021-11-12T08:31:49.420Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:49.420Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Larrañaga, Pedro and Lozano, Jose A and Mühlenbein, Heinz},\n journal = {Revista Iberoamericana de Inteligencia Artificial},\n number = {3}\n}
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\n \n\n \n \n \n \n \n Learning Bayesian networks in the space of structures by estimation of distribution algorithms.\n \n \n \n\n\n \n Blanco, R.; Inza, I.; and Larrañaga, P.\n\n\n \n\n\n\n International Journal of Intelligent Systems, 18: 205-220. 2003.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{\n title = {Learning Bayesian networks in the space of structures by estimation of distribution algorithms},\n type = {article},\n year = {2003},\n pages = {205-220},\n volume = {18},\n id = {9b9b527a-143b-3691-9a98-7b56a832bb60},\n created = {2021-11-12T08:31:55.005Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:55.005Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Blanco, R and Inza, I and Larrañaga, P},\n journal = {International Journal of Intelligent Systems}\n}
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\n  \n 2002\n \n \n (7)\n \n \n
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\n \n\n \n \n \n \n \n Advances in Supervised Classification based on Probabilistic Graphical Models.\n \n \n \n\n\n \n Inza, I.\n\n\n \n\n\n\n Ph.D. Thesis, 2002.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@phdthesis{\n title = {Advances in Supervised Classification based on Probabilistic Graphical Models},\n type = {phdthesis},\n year = {2002},\n institution = {University of the Basque Country},\n id = {603e5aec-dd4b-308b-bf4e-d01f765b22b9},\n created = {2021-11-12T08:30:06.485Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:06.485Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {phdthesis},\n private_publication = {false},\n bibtype = {phdthesis},\n author = {Inza, Iñaki}\n}
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\n \n\n \n \n \n \n \n Gene selection by sequential wrapper approaches in microarray cancer class prediction.\n \n \n \n\n\n \n Inza, I.; Sierra, B.; Blanco, R.; and Larrañaga, P.\n\n\n \n\n\n\n Journal of Intelligent and Fuzzy Systems, 12(1): 25-33. 2002.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Gene selection by sequential wrapper approaches in microarray cancer class prediction},\n type = {article},\n year = {2002},\n keywords = {isg_ehu,isg_jcr},\n pages = {25-33},\n volume = {12},\n id = {75252eb9-1441-3ef6-bcbb-9ceeec25c933},\n created = {2021-11-12T08:30:39.145Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:39.145Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Inza, Iñaki and Sierra, Basilio and Blanco, Rosa and Larrañaga, Pedro},\n journal = {Journal of Intelligent and Fuzzy Systems},\n number = {1}\n}
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\n \n\n \n \n \n \n \n Synergies between evolutionary computation and probabilistic graphical models.\n \n \n \n\n\n \n Larrañaga, P.; and Lozano, J., A.\n\n\n \n\n\n\n Int. J. Approx. Reasoning, 31(3): 155-156. 2002.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Synergies between evolutionary computation and probabilistic graphical models},\n type = {article},\n year = {2002},\n keywords = {isg_ehu,isg_jcr},\n pages = {155-156},\n volume = {31},\n id = {3a5d55e3-16f8-3a5d-8551-2a5bf9bfe071},\n created = {2021-11-12T08:30:41.692Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:41.692Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Larrañaga, Pedro and Lozano, Jose A},\n journal = {Int. J. Approx. Reasoning},\n number = {3}\n}
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\n \n\n \n \n \n \n \n Mathematical modelling of UMDAc algorithm with tournament selection.\n \n \n \n\n\n \n Gonzalez, C.; Lozano, J., A.; and Larrañaga, P.\n\n\n \n\n\n\n International Journal of Approximate Reasoning, 31(3): 313-340. 2002.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Mathematical modelling of UMDAc algorithm with tournament selection},\n type = {article},\n year = {2002},\n keywords = {isg_ehu,isg_jcr},\n pages = {313-340},\n volume = {31},\n id = {acda51c6-d2bb-375e-9ae4-2fee14b9f60e},\n created = {2021-11-12T08:30:48.055Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:48.055Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Gonzalez, Cristina and Lozano, Jose A and Larrañaga, Pedro},\n journal = {International Journal of Approximate Reasoning},\n number = {3}\n}
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\n \n\n \n \n \n \n \n Learning and simulation of Bayesian networks applied to inexact graph matching.\n \n \n \n\n\n \n Bengoetxea, E.; and Boeres, C.\n\n\n \n\n\n\n Pattern Recognition, 35(12): 2867-2880. 2002.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Learning and simulation of Bayesian networks applied to inexact graph matching},\n type = {article},\n year = {2002},\n keywords = {isg_ehu,isg_jcr},\n pages = {2867-2880},\n volume = {35},\n id = {cbc7a595-90de-327a-9e9a-8cfd7f707451},\n created = {2021-11-12T08:31:12.323Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:12.323Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Bengoetxea, Endika and Boeres, C},\n journal = {Pattern Recognition},\n number = {12}\n}
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\n \n\n \n \n \n \n \n Learning Recursive Bayesian Multinets for Data Clustering by Means of Constructive Induction.\n \n \n \n\n\n \n Peña, J., M.; Lozano, J., A.; and Larrañaga, P.\n\n\n \n\n\n\n Machine Learning, 47: 63-89. 2002.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Learning Recursive Bayesian Multinets for Data Clustering by Means of Constructive Induction},\n type = {article},\n year = {2002},\n keywords = {isg_ehu,isg_jcr},\n pages = {63-89},\n volume = {47},\n id = {bc1c1676-0cc5-3f0e-9d3c-0af0dc2697b1},\n created = {2021-11-12T08:31:26.286Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:26.286Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Peña, Jose M and Lozano, Jose A and Larrañaga, Pedro},\n journal = {Machine Learning}\n}
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\n \n\n \n \n \n \n \n Inexact Graph Matching Using Estimation of Distribution Algorithms Mise en Correspondence Inexacte de Graphes par Algorithmes d'Estimation de Distributions.\n \n \n \n\n\n \n Bengoetxea, E.\n\n\n \n\n\n\n Ph.D. Thesis, 2002.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@phdthesis{\n title = {Inexact Graph Matching Using Estimation of Distribution Algorithms Mise en Correspondence Inexacte de Graphes par Algorithmes d'Estimation de Distributions},\n type = {phdthesis},\n year = {2002},\n institution = {University of the Basque Country},\n id = {c6c7e431-eec0-3327-b121-17bdd7596cda},\n created = {2021-11-12T08:31:45.480Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:45.480Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {phdthesis},\n private_publication = {false},\n bibtype = {phdthesis},\n author = {Bengoetxea, Endika}\n}
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\n  \n 2001\n \n \n (5)\n \n \n
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\n \n\n \n \n \n \n \n Performance evaluation of compromise conditional Gaussian networks for data clustering.\n \n \n \n\n\n \n Peña, J., M.; Lozano, J., A.; and Larrañaga, P.\n\n\n \n\n\n\n International Journal of Approximate Reasoning, 28(1): 23-50. 2001.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Performance evaluation of compromise conditional Gaussian networks for data clustering},\n type = {article},\n year = {2001},\n keywords = {isg_ehu,isg_jcr},\n pages = {23-50},\n volume = {28},\n id = {d751df90-42dd-3703-8ede-cb3c00a5868e},\n created = {2021-11-12T08:30:08.374Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:08.374Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Peña, Jose M and Lozano, Jose A and Larrañaga, Pedro},\n journal = {International Journal of Approximate Reasoning},\n number = {1}\n}
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\n \n\n \n \n \n \n \n Feature subset selection by Bayesian networks: a comparison with genetic and sequential algorithms.\n \n \n \n\n\n \n Inza, I.; Larrañaga, P.; and Sierra, B.\n\n\n \n\n\n\n Artificial Intelligence in Medicine, 27(2). 2001.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
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\n \n\n \n \n \n \n \n Using Bayesian networks in the construction of a bi-level multi-classifier. A case study using intensive care unit patients data.\n \n \n \n\n\n \n Sierra, B.; Serrano, N.; Larrañaga, P.; Plasencia, E., J.; Inza, I.; Jiménez, J., J.; Revuelta, P.; and Mora, M., L.\n\n\n \n\n\n\n Artificial Intelligence in Medicine, 22(3): 233-248. 2001.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
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\n \n\n \n \n \n \n \n Dimensionality reduction in Unsupervised learning of conditional Gaussian networks.\n \n \n \n\n\n \n Peña, J., M.; Lozano, J., A.; Larrañaga, P.; and Inza, I.\n\n\n \n\n\n\n IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(6): 590-603. 2001.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Dimensionality reduction in Unsupervised learning of conditional Gaussian networks},\n type = {article},\n year = {2001},\n keywords = {isg_ehu,isg_jcr},\n pages = {590-603},\n volume = {23},\n id = {0fc124b2-df62-3b3f-b73e-1673bda6629a},\n created = {2021-11-12T08:31:38.418Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:38.418Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Peña, Jose M and Lozano, Jose A and Larrañaga, Pedro and Inza, Iñaki},\n journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},\n number = {6}\n}
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\n \n\n \n \n \n \n \n Feature subset selection by genetic algorithms and estimation of distribution algorithms. A case study in the survival of cirrhotic patients treated with TIPS.\n \n \n \n\n\n \n Inza, I.; Merino, M.; Larrañaga, P.; Quiroga, J.; Sierra, B.; and Girala, M.\n\n\n \n\n\n\n Artificial Intelligence in Medicine, 23(2). 2001.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Feature subset selection by genetic algorithms and estimation of distribution algorithms. A case study in the survival of cirrhotic patients treated with TIPS},\n type = {article},\n year = {2001},\n keywords = {isg_ehu,isg_jcr},\n volume = {23},\n id = {3c162748-81be-3ef4-9cfe-5132a83e5cab},\n created = {2021-11-12T08:31:55.703Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:55.703Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Inza, Iñaki and Merino, Marisa and Larrañaga, Pedro and Quiroga, Jorge and Sierra, Basilio and Girala, Marcos},\n journal = {Artificial Intelligence in Medicine},\n number = {2}\n}
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\n  \n 2000\n \n \n (4)\n \n \n
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\n \n\n \n \n \n \n \n Analyzing the PBIL Algorithm by Means of Discrete Dynamical Systems.\n \n \n \n\n\n \n Gonzalez, C.; Lozano, J., A.; and Larrañaga, P.\n\n\n \n\n\n\n Complex Systems, 12: 465-479. 2000.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Analyzing the PBIL Algorithm by Means of Discrete Dynamical Systems},\n type = {article},\n year = {2000},\n keywords = {isg_ehu,isg_jcr},\n pages = {465-479},\n volume = {12},\n id = {a70af7ab-608f-3d0e-8fef-c7f8d1f44ed9},\n created = {2021-11-12T08:30:24.528Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:24.528Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Gonzalez, Cristina and Lozano, Jose A and Larrañaga, Pedro},\n journal = {Complex Systems}\n}
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\n \n\n \n \n \n \n \n An improved Bayesian structural EM algorithm for learning Bayesian networks for clustering.\n \n \n \n\n\n \n Peña, J., M.; Lozano, J., A.; and Larrañaga, P.\n\n\n \n\n\n\n Pattern Recognition Letters, 21(8): 779-786. 2000.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {An improved Bayesian structural EM algorithm for learning Bayesian networks for clustering},\n type = {article},\n year = {2000},\n keywords = {isg_ehu,isg_jcr},\n pages = {779-786},\n volume = {21},\n id = {dd37bda6-039d-329f-bde2-4a3dbdaa3630},\n created = {2021-11-12T08:30:34.785Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:34.785Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Peña, Jose M and Lozano, Jose A and Larrañaga, Pedro},\n journal = {Pattern Recognition Letters},\n number = {8}\n}
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\n \n\n \n \n \n \n \n Feature subset selection by Bayesian networks based optimization.\n \n \n \n\n\n \n Inza, I.; Larrañaga, P.; Etxeberria, R.; and Sierra, B.\n\n\n \n\n\n\n Artificial Intelligence, 123(01/02/2016). 2000.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
\n 
@article{\n title = {Feature subset selection by Bayesian networks based optimization},\n type = {article},\n year = {2000},\n keywords = {isg_ehu,isg_jcr},\n volume = {123},\n id = {1635a176-3d22-398f-889c-f2255a336999},\n created = {2021-11-12T08:30:55.876Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:55.876Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Inza, Iñaki and Larrañaga, Pedro and Etxeberria, Ramón and Sierra, Basilio},\n journal = {Artificial Intelligence},\n number = {01/02/2016}\n}
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\n \n\n \n \n \n \n \n Aportaciones Metodológicas a la Clasificación Supervisada.\n \n \n \n\n\n \n Sierra, B.\n\n\n \n\n\n\n Ph.D. Thesis, 2000.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@phdthesis{\n title = {Aportaciones Metodológicas a la Clasificación Supervisada},\n type = {phdthesis},\n year = {2000},\n institution = {University of the Basque Country},\n id = {77ce9dfb-10ce-3c6f-a89f-9d197d51191c},\n created = {2021-11-12T08:31:22.432Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:22.432Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {phdthesis},\n private_publication = {false},\n bibtype = {phdthesis},\n author = {Sierra, Basilio}\n}
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\n  \n 1999\n \n \n (2)\n \n \n
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\n \n\n \n \n \n \n \n Representing the joint behaviour of machine learning inducers by Bayesian networks.\n \n \n \n\n\n \n Inza, I.; Larrañaga, P.; Sierra, B.; Etxeberria, R.; Lozano, J., A.; and Peña, J., M.\n\n\n \n\n\n\n Pattern Recognition Letters, 20(11-13). 1999.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Representing the joint behaviour of machine learning inducers by Bayesian networks},\n type = {article},\n year = {1999},\n keywords = {isg_ehu,isg_jcr},\n volume = {20},\n id = {b3689afc-5e21-32aa-bb33-69a6103c1203},\n created = {2021-11-12T08:31:48.312Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:31:48.312Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Inza, Iñaki and Larrañaga, Pedro and Sierra, Basilio and Etxeberria, Ramón and Lozano, Jose A and Peña, Jose M},\n journal = {Pattern Recognition Letters},\n number = {11-13}\n}
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\n \n\n \n \n \n \n \n Evolutionary algorithms for the travelling salesman problem: A review of representations and operators.\n \n \n \n\n\n \n Larrañaga, P.; Kuijpers, C.; Murga, R.; Inza, I.; and Dizdarevic, S.\n\n\n \n\n\n\n Artificial Intelligence Review, 13(2): 129-170. 1999.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Evolutionary algorithms for the travelling salesman problem: A review of representations and operators},\n type = {article},\n year = {1999},\n keywords = {isg_ehu,isg_jcr},\n pages = {129-170},\n volume = {13},\n id = {33f15353-75bb-3202-a5a9-fb6d6b9e640b},\n created = {2021-11-12T08:32:02.603Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:32:02.603Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n bibtype = {article},\n author = {Larrañaga, Pedro and Kuijpers, Cindy and Murga, Roberto and Inza, Iñaki and Dizdarevic, Sejla},\n journal = {Artificial Intelligence Review},\n number = {2}\n}
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\n  \n 1998\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n Algoritmos Genéticos Aplicados a la Clasificación no Supervisada.\n \n \n \n\n\n \n Lozano Alonso, J., A.\n\n\n \n\n\n\n Ph.D. Thesis, 1998.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@phdthesis{\n title = {Algoritmos Genéticos Aplicados a la Clasificación no Supervisada},\n type = {phdthesis},\n year = {1998},\n institution = {University of the Basque Country},\n id = {ce8e8aea-0539-370a-8740-29a40ca13918},\n created = {2021-11-12T08:30:49.847Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:49.847Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {phdthesis},\n private_publication = {false},\n bibtype = {phdthesis},\n author = {Lozano Alonso, Jose Antonio}\n}
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\n  \n 1995\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n Aprendizaje Estructural y Descomposición de Redes Bayesianas Via Algoritmos Genéticos.\n \n \n \n\n\n \n Larrañaga, P.\n\n\n \n\n\n\n Ph.D. Thesis, 1995.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@phdthesis{\n title = {Aprendizaje Estructural y Descomposición de Redes Bayesianas Via Algoritmos Genéticos},\n type = {phdthesis},\n year = {1995},\n institution = {University of the Basque Country},\n id = {f58bdc1a-4c5a-3bc8-b862-d26e01205545},\n created = {2021-11-12T08:30:41.438Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2021-11-12T08:30:41.438Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {phdthesis},\n private_publication = {false},\n bibtype = {phdthesis},\n author = {Larrañaga, Pedro}\n}
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\n  \n undefined\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Early prediction of Ibex 35 movements.\n \n \n \n \n\n\n \n Miranda García, I., M.; Segovia-Vargas, M.; Mori, U.; and Lozano, J., A.\n\n\n \n\n\n\n Journal of Forecasting, n/a(n/a). .\n \n\n\n\n
\n\n\n\n \n \n \"EarlyWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {Early prediction of Ibex 35 movements},\n type = {article},\n keywords = {artificial intelligence,high-frequency data,intraday pattern,price discovery,stock price prediction,trading hours},\n volume = {n/a},\n websites = {https://onlinelibrary.wiley.com/doi/abs/10.1002/for.2933},\n id = {7466d404-8adf-31c8-9909-7b4d94c84530},\n created = {2023-05-23T15:20:54.193Z},\n file_attached = {false},\n profile_id = {789246de-927b-32cc-ae4f-1b7e2b31674c},\n group_id = {e3c82d43-35db-3bbb-b28a-0fd521d70498},\n last_modified = {2023-05-23T15:20:54.193Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n source_type = {article},\n private_publication = {false},\n abstract = {Abstract In this paper, we examine the early predictability of the market's directional movement using intraday high-frequency data (695,764 observations) from an stock index (Ibex 35 Index) to provide, either private or institutional investors, an early warning system based on an ``early indicator'' of the financial market fluctuations with an optimal combination of the two more relevant variables for this strategy, accuracy, and earliness. A novel supervised machine learning early classification technique (Artificial Intelligence) has been applied, for the first time, to the high-frequency time series of both price and certain technical indicators. The results obtained allow us to assert that the intraday movement of the Ibex 35 can be predicted with acceptable levels of accuracy 24 min after the start of the session and to establish certain informative intraday hourly patterns. Consequently, different indicators of precision and earliness in the session are generated, obtaining that, after a certain point in the session, no gains in precision are generated.},\n bibtype = {article},\n author = {Miranda García, I Marta and Segovia-Vargas, María-Jesús and Mori, Usue and Lozano, Jose A.},\n doi = {https://doi.org/10.1002/for.2933},\n journal = {Journal of Forecasting},\n number = {n/a}\n}
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\n Abstract In this paper, we examine the early predictability of the market's directional movement using intraday high-frequency data (695,764 observations) from an stock index (Ibex 35 Index) to provide, either private or institutional investors, an early warning system based on an ``early indicator'' of the financial market fluctuations with an optimal combination of the two more relevant variables for this strategy, accuracy, and earliness. A novel supervised machine learning early classification technique (Artificial Intelligence) has been applied, for the first time, to the high-frequency time series of both price and certain technical indicators. The results obtained allow us to assert that the intraday movement of the Ibex 35 can be predicted with acceptable levels of accuracy 24 min after the start of the session and to establish certain informative intraday hourly patterns. Consequently, different indicators of precision and earliness in the session are generated, obtaining that, after a certain point in the session, no gains in precision are generated.\n
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