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  2022 (4)
Examining Supervised Machine Learning Methods for Integer Link Weight Prediction Using Node Metadata. Mori, L.; O'Hara, K.; Pujol, T. A.; and Ventresca, M. Entropy, 24(6): 842. 2022.
Examining Supervised Machine Learning Methods for Integer Link Weight Prediction Using Node Metadata. [link]Link   Examining Supervised Machine Learning Methods for Integer Link Weight Prediction Using Node Metadata. [link]Paper   link   bibtex  
Investigating cognitive ability using action-based models of structural brain networks. Arora, V.; Amico, E.; Goñi, J.; and Ventresca, M. J. Complex Networks, 10(4). 06 2022.
Investigating cognitive ability using action-based models of structural brain networks. [link]Link   Investigating cognitive ability using action-based models of structural brain networks. [link]Paper   link   bibtex  
Identifying the source of an epidemic using particle swarm optimization. MaGee, J.; Arora, V.; and Ventresca, M. In Fieldsend, J. E.; and Wagner, M., editor(s), GECCO, pages 1237-1244, 2022. ACM
Identifying the source of an epidemic using particle swarm optimization. [link]Link   Identifying the source of an epidemic using particle swarm optimization. [link]Paper   link   bibtex  
Social Influence Network Simulation Design Affects Behavior of Aggregated Entropy. Garee, M. J.; Wan, H.; and Ventresca, M. IEEE Trans. Comput. Soc. Syst., 9(2): 594-604. 2022.
Social Influence Network Simulation Design Affects Behavior of Aggregated Entropy. [link]Link   Social Influence Network Simulation Design Affects Behavior of Aggregated Entropy. [link]Paper   link   bibtex  
  2021 (3)
Geodesic Distance on Optimally Regularized Functional Connectomes Uncovers Individual Fingerprints. Abbas, K.; Liu, M.; Venkatesh, M.; Amico, E.; Kaplan, A. D.; Ventresca, M.; Pessoa, L.; Harezlak, J.; and Goñi, J. Brain Connect., 11(5): 333-348. 2021.
Geodesic Distance on Optimally Regularized Functional Connectomes Uncovers Individual Fingerprints. [link]Link   Geodesic Distance on Optimally Regularized Functional Connectomes Uncovers Individual Fingerprints. [link]Paper   link   bibtex  
Supervised Link Weight Prediction Using Node Metadata. Mori, L.; Ventresca, M.; and Pujol, T. A. In Benito, R. M.; Cherifi, C.; Cherifi, H.; Moro, E.; Rocha, L. M.; and Sales-Pardo, M., editor(s), COMPLEX NETWORKS, volume 1016, of Studies in Computational Intelligence, pages 496-507, 2021. Springer
Supervised Link Weight Prediction Using Node Metadata. [link]Link   Supervised Link Weight Prediction Using Node Metadata. [link]Paper   link   bibtex  
A Graph-Based Ant Algorithm for the Winner Determination Problem in Combinatorial Auctions. Ray, A.; Ventresca, M.; and Kannan, K. N. Inf. Syst. Res., 32(4): 1099-1114. 2021.
A Graph-Based Ant Algorithm for the Winner Determination Problem in Combinatorial Auctions. [link]Link   A Graph-Based Ant Algorithm for the Winner Determination Problem in Combinatorial Auctions. [link]Paper   link   bibtex  
  2020 (3)
Improving neighbor-based collaborative filtering by using a hybrid similarity measurement. Wang, D.; Yih, Y.; and Ventresca, M. Expert Syst. Appl., 160: 113651. 2020.
Improving neighbor-based collaborative filtering by using a hybrid similarity measurement. [link]Link   Improving neighbor-based collaborative filtering by using a hybrid similarity measurement. [link]Paper   link   bibtex  
Modeling Communication Processes in the Human Connectome through Cooperative Learning. Tipnis, U.; Amico, E.; Ventresca, M.; and Goñi, J. IEEE Trans. Netw. Sci. Eng., 7(1): 476-488. 2020.
Modeling Communication Processes in the Human Connectome through Cooperative Learning. [link]Link   Modeling Communication Processes in the Human Connectome through Cooperative Learning. [link]Paper   link   bibtex  
Examining the variability in network populations and its role in generative models. Arora, V.; Guo, D.; Dunbar, K. D.; and Ventresca, M. Netw. Sci., 8(S1): S43-S64. 2020.
Examining the variability in network populations and its role in generative models. [link]Link   Examining the variability in network populations and its role in generative models. [link]Paper   link   bibtex  
  2019 (2)
System of Systems Approach for Maintaining Wastewater System. Altarabsheh, A.; Kandil, A.; Abraham, D. M.; DeLaurentis, D.; and Ventresca, M. J. Comput. Civ. Eng., 33(3). 2019.
System of Systems Approach for Maintaining Wastewater System. [link]Link   System of Systems Approach for Maintaining Wastewater System. [link]Paper   link   bibtex  
Multiple traveling salesman problem with drones: Mathematical model and heuristic approach. Kitjacharoenchai, P.; Ventresca, M.; Moshref-Javadi, M.; Lee, S.; Tanchoco, J. M. A.; and Brunese, P. A. Comput. Ind. Eng., 129: 14-30. 2019.
Multiple traveling salesman problem with drones: Mathematical model and heuristic approach. [link]Link   Multiple traveling salesman problem with drones: Mathematical model and heuristic approach. [link]Paper   link   bibtex  
  2018 (9)
New Multiobjective Optimization Approach to Rehabilitate and Maintain Sewer Networks Based on Whole Lifecycle Behavior. Altarabsheh, A.; Kandil, A.; and Ventresca, M. J. Comput. Civ. Eng., 32(1). 2018.
New Multiobjective Optimization Approach to Rehabilitate and Maintain Sewer Networks Based on Whole Lifecycle Behavior. [link]Link   New Multiobjective Optimization Approach to Rehabilitate and Maintain Sewer Networks Based on Whole Lifecycle Behavior. [link]Paper   link   bibtex  
Evolutionary Algorithm for Selecting Wastewater System Configuration. Altarabsheh, A.; Ventresca, M.; and Kandil, A. J. Comput. Civ. Eng., 32(6). 2018.
Evolutionary Algorithm for Selecting Wastewater System Configuration. [link]Link   Evolutionary Algorithm for Selecting Wastewater System Configuration. [link]Paper   link   bibtex  
New Approach for Critical Pipe Prioritization in Wastewater Asset Management Planning. Altarabsheh, A.; Ventresca, M.; and Kandil, A. J. Comput. Civ. Eng., 32(5). 2018.
New Approach for Critical Pipe Prioritization in Wastewater Asset Management Planning. [link]Link   New Approach for Critical Pipe Prioritization in Wastewater Asset Management Planning. [link]Paper   link   bibtex  
Modeling topologically resilient supply chain networks. Arora, V.; and Ventresca, M. Appl. Netw. Sci., 3(1): 19:1-19:20. 2018.
Modeling topologically resilient supply chain networks. [link]Link   Modeling topologically resilient supply chain networks. [link]Paper   link   bibtex  
Evaluating the Natural Variability in Generative Models for Complex Networks. Arora, V.; and Ventresca, M. In Aiello, L. M.; Cherifi, C.; Cherifi, H.; Lambiotte, R.; Lió, P.; and Rocha, L. M., editor(s), COMPLEX NETWORKS (1), volume 812, of Studies in Computational Intelligence, pages 743-754, 2018. Springer
Evaluating the Natural Variability in Generative Models for Complex Networks. [link]Link   Evaluating the Natural Variability in Generative Models for Complex Networks. [link]Paper   link   bibtex  
The bi-objective critical node detection problem. Ventresca, M.; Harrison, K. R.; and Ombuki-Berman, B. M. Eur. J. Oper. Res., 265(3): 895-908. 2018.
The bi-objective critical node detection problem. [link]Link   The bi-objective critical node detection problem. [link]Paper   link   bibtex  
A Mechanism Design Approach to Blockchain Protocols. Ray, A.; Ventresca, M.; and Wan, H. In iThings/GreenCom/CPSCom/SmartData, pages 1603-1608, 2018. IEEE
A Mechanism Design Approach to Blockchain Protocols. [link]Link   A Mechanism Design Approach to Blockchain Protocols. [link]Paper   link   bibtex  
Using Algorithmic Complexity to Differentiate Cognitive States in fMRI. Ventresca, M. In Aiello, L. M.; Cherifi, C.; Cherifi, H.; Lambiotte, R.; Lió, P.; and Rocha, L. M., editor(s), COMPLEX NETWORKS (2), volume 813, of Studies in Computational Intelligence, pages 663-674, 2018. Springer
Using Algorithmic Complexity to Differentiate Cognitive States in fMRI. [link]Link   Using Algorithmic Complexity to Differentiate Cognitive States in fMRI. [link]Paper   link   bibtex  
An Ant Colony Approach for the Winner Determination Problem. Ray, A.; and Ventresca, M. In Liefooghe, A.; and López-Ibáñez, M., editor(s), EvoCOP, volume 10782, of Lecture Notes in Computer Science, pages 174-188, 2018. Springer
An Ant Colony Approach for the Winner Determination Problem. [link]Link   An Ant Colony Approach for the Winner Determination Problem. [link]Paper   link   bibtex  
  2017 (3)
Dynamic Generative Model of the Human Brain in Resting-State. Guo, D.; Arora, V.; Amico, E.; Goñi, J.; and Ventresca, M. In Cherifi, C.; Cherifi, H.; Karsai, M.; and Musolesi, M., editor(s), COMPLEX NETWORKS, volume 689, of Studies in Computational Intelligence, pages 1271-1283, 2017. Springer
Dynamic Generative Model of the Human Brain in Resting-State. [link]Link   Dynamic Generative Model of the Human Brain in Resting-State. [link]Paper   link   bibtex  
Action-Based Model for Topologically Resilient Supply Networks. Arora, V.; and Ventresca, M. In Cherifi, C.; Cherifi, H.; Karsai, M.; and Musolesi, M., editor(s), COMPLEX NETWORKS, volume 689, of Studies in Computational Intelligence, pages 658-669, 2017. Springer
Action-Based Model for Topologically Resilient Supply Networks. [link]Link   Action-Based Model for Topologically Resilient Supply Networks. [link]Paper   link   bibtex  
Attacking Unexplored Networks - The Probe-and-Attack Problem. Chong, B. H.; and Ventresca, M. In Cherifi, C.; Cherifi, H.; Karsai, M.; and Musolesi, M., editor(s), COMPLEX NETWORKS, volume 689, of Studies in Computational Intelligence, pages 692-703, 2017. Springer
Attacking Unexplored Networks - The Probe-and-Attack Problem. [link]Link   Attacking Unexplored Networks - The Probe-and-Attack Problem. [link]Paper   link   bibtex  
  2016 (2)
A meta-analysis of centrality measures for comparing and generating complex network models. Harrison, K. R.; Ventresca, M.; and Ombuki-Berman, B. M. J. Comput. Sci., 17: 205-215. 2016.
A meta-analysis of centrality measures for comparing and generating complex network models. [link]Link   A meta-analysis of centrality measures for comparing and generating complex network models. [link]Paper   link   bibtex  
A Multi-objective Optimization Approach for Generating Complex Networks. Arora, V.; and Ventresca, M. In Friedrich, T.; Neumann, F.; and Sutton, A. M., editor(s), GECCO (Companion), pages 15-16, 2016. ACM
A Multi-objective Optimization Approach for Generating Complex Networks. [link]Link   A Multi-objective Optimization Approach for Generating Complex Networks. [link]Paper   link   bibtex  
  2015 (3)
Investigating Fitness Measures for the Automatic Construction of Graph Models. Harrison, K. R.; Ventresca, M.; and Ombuki-Berman, B. M. In Mora, A. M.; and Squillero, G., editor(s), EvoApplications, volume 9028, of Lecture Notes in Computer Science, pages 189-200, 2015. Springer
Investigating Fitness Measures for the Automatic Construction of Graph Models. [link]Link   Investigating Fitness Measures for the Automatic Construction of Graph Models. [link]Paper   link   bibtex  
An Experimental Evaluation of Multi-objective Evolutionary Algorithms for Detecting Critical Nodes in Complex Networks. Ventresca, M.; Harrison, K. R.; and Ombuki-Berman, B. M. In Mora, A. M.; and Squillero, G., editor(s), EvoApplications, volume 9028, of Lecture Notes in Computer Science, pages 164-176, 2015. Springer
An Experimental Evaluation of Multi-objective Evolutionary Algorithms for Detecting Critical Nodes in Complex Networks. [link]Link   An Experimental Evaluation of Multi-objective Evolutionary Algorithms for Detecting Critical Nodes in Complex Networks. [link]Paper   link   bibtex  
Network robustness versus multi-strategy sequential attack. Ventresca, M.; and Aleman, D. M. J. Complex Networks, 3(1): 126-146. 2015.
Network robustness versus multi-strategy sequential attack. [link]Link   Network robustness versus multi-strategy sequential attack. [link]Paper   link   bibtex  
  2014 (7)
Genetic Programming for the Automatic Inference of Graph Models for Complex Networks. Bailey, A.; Ventresca, M.; and Ombuki-Berman, B. M. IEEE Trans. Evol. Comput., 18(3): 405-419. 2014.
Genetic Programming for the Automatic Inference of Graph Models for Complex Networks. [link]Link   Genetic Programming for the Automatic Inference of Graph Models for Complex Networks. [link]Paper   link   bibtex  
A derandomized approximation algorithm for the critical node detection problem. Ventresca, M.; and Aleman, D. M. Comput. Oper. Res., 43: 261-270. 2014.
A derandomized approximation algorithm for the critical node detection problem. [link]Link   A derandomized approximation algorithm for the critical node detection problem. [link]Paper   link   bibtex  
A randomized algorithm with local search for containment of pandemic disease spread. Ventresca, M.; and Aleman, D. M. Comput. Oper. Res., 48: 11-19. 2014.
A randomized algorithm with local search for containment of pandemic disease spread. [link]Link   A randomized algorithm with local search for containment of pandemic disease spread. [link]Paper   link   bibtex  
Network robustness versus multi-strategy sequential attack. Ventresca, M.; and Aleman, D. Journal of Complex Networks,cnu010+. 05 2014.
Network robustness versus multi-strategy sequential attack [link]Paper   doi   link   bibtex   abstract  
A Region Growing Algorithm for Detecting Critical Nodes. Ventresca, M.; and Aleman, D. M. In Zhang, Z.; Wu, L.; Xu, W.; and Du, D., editor(s), COCOA, volume 8881, of Lecture Notes in Computer Science, pages 593-602, 2014. Springer
A Region Growing Algorithm for Detecting Critical Nodes. [link]Link   A Region Growing Algorithm for Detecting Critical Nodes. [link]Paper   link   bibtex  
A Fast Greedy Algorithm for the Critical Node Detection Problem. Ventresca, M.; and Aleman, D. M. In Zhang, Z.; Wu, L.; Xu, W.; and Du, D., editor(s), COCOA, volume 8881, of Lecture Notes in Computer Science, pages 603-612, 2014. Springer
A Fast Greedy Algorithm for the Critical Node Detection Problem. [link]Link   A Fast Greedy Algorithm for the Critical Node Detection Problem. [link]Paper   link   bibtex  
Approximation Algorithms for Detecting Critical Nodes. Ventresca, M.; and Aleman, D. M. In Butenko, S.; Pasiliao, E. L.; and Shylo, V., editor(s), Examining Robustness and Vulnerability of Networked Systems, volume 37, of NATO Science for Peace and Security Series, D: Information and Communication Security, pages 289-305. IOS Press, 2014.
Approximation Algorithms for Detecting Critical Nodes. [link]Link   Approximation Algorithms for Detecting Critical Nodes. [link]Paper   link   bibtex  
  2013 (3)
Evaluation of strategies to mitigate contagion spread using social network characteristics. Ventresca, M.; and Aleman, D. M. Soc. Networks, 35(1): 75-88. 2013.
Evaluation of strategies to mitigate contagion spread using social network characteristics. [link]Link   Evaluation of strategies to mitigate contagion spread using social network characteristics. [link]Paper   link   bibtex  
Automatic inference of hierarchical graph models using genetic programming with an application to cortical networks. Bailey, A.; Ombuki-Berman, B. M.; and Ventresca, M. In Blum, C.; and Alba, E., editor(s), GECCO, pages 893-900, 2013. ACM
Automatic inference of hierarchical graph models using genetic programming with an application to cortical networks. [link]Link   Automatic inference of hierarchical graph models using genetic programming with an application to cortical networks. [link]Paper   link   bibtex  
Predicting Genetic Algorithm Performance on the Vehicle Routing Problem Using Information Theoretic Landscape Measures. Ventresca, M.; Ombuki-Berman, B. M.; and Runka, A. In Middendorf, M.; and Blum, C., editor(s), EvoCOP, volume 7832, of Lecture Notes in Computer Science, pages 214-225, 2013. Springer
Predicting Genetic Algorithm Performance on the Vehicle Routing Problem Using Information Theoretic Landscape Measures. [link]Link   Predicting Genetic Algorithm Performance on the Vehicle Routing Problem Using Information Theoretic Landscape Measures. [link]Paper   link   bibtex  
  2012 (3)
Global search algorithms using a combinatorial unranking-based problem representation for the critical node detection problem. Ventresca, M. Comput. Oper. Res., 39(11): 2763-2775. 2012.
Global search algorithms using a combinatorial unranking-based problem representation for the critical node detection problem. [link]Link   Global search algorithms using a combinatorial unranking-based problem representation for the critical node detection problem. [link]Paper   link   bibtex  
Automatic generation of graph models for complex networks by genetic programming. Bailey, A.; Ventresca, M.; and Ombuki-Berman, B. M. In Soule, T.; and Moore, J. H., editor(s), GECCO, pages 711-718, 2012. ACM
Automatic generation of graph models for complex networks by genetic programming. [link]Link   Automatic generation of graph models for complex networks by genetic programming. [link]Paper   link   bibtex  
An intuitive distance-based explanation of opposition-based sampling. Rahnamayan, S.; Wang, G. G.; and Ventresca, M. Appl. Soft Comput., 12(9): 2828-2839. 2012.
An intuitive distance-based explanation of opposition-based sampling. [link]Link   An intuitive distance-based explanation of opposition-based sampling. [link]Paper   link   bibtex  
  2011 (1)
Enhancing particle swarm optimization using generalized opposition-based learning. Wang, H.; Wu, Z.; Rahnamayan, S.; Liu, Y.; and Ventresca, M. Inf. Sci., 181(20): 4699-4714. 2011.
Enhancing particle swarm optimization using generalized opposition-based learning. [link]Link   Enhancing particle swarm optimization using generalized opposition-based learning. [link]Paper   link   bibtex  
  2010 (1)
A note on "Opposition versus randomness in soft computing techniques" [Appl. Soft Comput 8 (2) (2008) 906-918]. Ventresca, M.; Rahnamayan, S.; and Tizhoosh, H. R. Appl. Soft Comput., 10(3): 956-957. 2010.
A note on "Opposition versus randomness in soft computing techniques" [Appl. Soft Comput 8 (2) (2008) 906-918]. [link]Link   A note on "Opposition versus randomness in soft computing techniques" [Appl. Soft Comput 8 (2) (2008) 906-918]. [link]Paper   link   bibtex  
  2009 (3)
Improving gradient-based learning algorithms for large scale feedforward networks. Ventresca, M.; and Tizhoosh, H. R. In IJCNN, pages 3212-3219, 2009. IEEE Computer Society
Improving gradient-based learning algorithms for large scale feedforward networks. [link]Link   Improving gradient-based learning algorithms for large scale feedforward networks. [link]Paper   link   bibtex  
Symmetry Induction in Computational Intelligence. Ventresca, M. Ph.D. Thesis, University of Waterloo, Ontario, Canada, 2009. base-search.net (ftunivwaterloo:oai:uwspace.uwaterloo.ca:10012/4845)
Symmetry Induction in Computational Intelligence. [link]Link   link   bibtex  
A search space analysis for the waste collection vehicle routing problem with time windows. Runka, A.; Ombuki-Berman, B. M.; and Ventresca, M. In Rothlauf, F., editor(s), GECCO, pages 1813-1814, 2009. ACM
A search space analysis for the waste collection vehicle routing problem with time windows. [link]Link   A search space analysis for the waste collection vehicle routing problem with time windows. [link]Paper   link   bibtex  
  2008 (5)
A diversity maintaining population-based incremental learning algorithm. Ventresca, M.; and Tizhoosh, H. R. Inf. Sci., 178(21): 4038-4056. 2008.
A diversity maintaining population-based incremental learning algorithm. [link]Link   A diversity maintaining population-based incremental learning algorithm. [link]Paper   link   bibtex  
Numerical condition of feedforward networks with opposite transfer functions. Ventresca, M.; and Tizhoosh, H. R. In IJCNN, pages 3233-3240, 2008. IEEE
Numerical condition of feedforward networks with opposite transfer functions. [link]Link   Numerical condition of feedforward networks with opposite transfer functions. [link]Paper   link   bibtex  
Two Frameworks for Improving Gradient-Based Learning Algorithms. Ventresca, M.; and Tizhoosh, H. R. In Tizhoosh, H. R.; and Ventresca, M., editor(s), Oppositional Concepts in Computational Intelligence, volume 155, of Studies in Computational Intelligence, pages 255-284. Springer, 2008.
Two Frameworks for Improving Gradient-Based Learning Algorithms. [link]Link   Two Frameworks for Improving Gradient-Based Learning Algorithms. [link]Paper   link   bibtex  
Introduction. Tizhoosh, H. R.; and Ventresca, M. In Tizhoosh, H. R.; and Ventresca, M., editor(s), Oppositional Concepts in Computational Intelligence, volume 155, of Studies in Computational Intelligence, pages 1-8. Springer, 2008.
Introduction. [link]Link   Introduction. [link]Paper   link   bibtex  
Opposition-Based Computing. Tizhoosh, H. R.; Ventresca, M.; and Rahnamayan, S. In Tizhoosh, H. R.; and Ventresca, M., editor(s), Oppositional Concepts in Computational Intelligence, volume 155, of Studies in Computational Intelligence, pages 11-28. Springer, 2008.
Opposition-Based Computing. [link]Link   Opposition-Based Computing. [link]Paper   link   bibtex  
  2007 (4)
Simulated Annealing with Opposite Neighbors. Ventresca, M.; and Tizhoosh, H. R. In FOCI, pages 186-192, 2007. IEEE
Simulated Annealing with Opposite Neighbors. [link]Link   Simulated Annealing with Opposite Neighbors. [link]Paper   link   bibtex  
Opposite Transfer Functions and Backpropagation Through Time. Ventresca, M.; and Tizhoosh, H. R. In FOCI, pages 570-577, 2007. IEEE
Opposite Transfer Functions and Backpropagation Through Time. [link]Link   Opposite Transfer Functions and Backpropagation Through Time. [link]Paper   link   bibtex  
Epistasis in Multi-Objective Evolutionary Recurrent Neuro-Controllers. Ventresca, M.; and Ombuki-Berman, B. M. In ALIFE, pages 77-84, 2007. IEEE
Epistasis in Multi-Objective Evolutionary Recurrent Neuro-Controllers. [link]Link   Epistasis in Multi-Objective Evolutionary Recurrent Neuro-Controllers. [link]Paper   link   bibtex  
Search Difficulty of Two-Connected Ring-based Topological Network Designs. Ombuki-Berman, B. M.; and Ventresca, M. In FOCI, pages 267-274, 2007. IEEE
Search Difficulty of Two-Connected Ring-based Topological Network Designs. [link]Link   Search Difficulty of Two-Connected Ring-based Topological Network Designs. [link]Paper   link   bibtex  
  2006 (4)
Search Space Analysis of Recurrent Spiking and Continuous-time Neural Networks. Ventresca, M.; and Ombuki-Berman, B. M. In IJCNN, pages 4514-4521, 2006. IEEE
Search Space Analysis of Recurrent Spiking and Continuous-time Neural Networks. [link]Link   Search Space Analysis of Recurrent Spiking and Continuous-time Neural Networks. [link]Paper   link   bibtex  
Improving the Convergence of Backpropagation by Opposite Transfer Functions. Ventresca, M.; and Tizhoosh, H. R. In IJCNN, pages 4777-4784, 2006. IEEE
Improving the Convergence of Backpropagation by Opposite Transfer Functions. [link]Link   Improving the Convergence of Backpropagation by Opposite Transfer Functions. [link]Paper   link   bibtex  
Optimized Memory Assignment for DSPs. Gréwal, G.; Coros, S.; Banerji, D. K.; Morton, A.; and Ventresca, M. In IEEE Congress on Evolutionary Computation, pages 64-72, 2006. IEEE
Optimized Memory Assignment for DSPs. [link]Link   Optimized Memory Assignment for DSPs. [link]Paper   link   bibtex  
A Memetic Algorithm for Performing Memory Assignment in Dual-Bank DSPs. Gréwal, G.; Coros, S.; and Ventresca, M. International Journal of Computational Intelligence and Applications, 6(4): 473-497. 2006.
A Memetic Algorithm for Performing Memory Assignment in Dual-Bank DSPs. [link]Link   A Memetic Algorithm for Performing Memory Assignment in Dual-Bank DSPs. [link]Paper   link   bibtex  
  2005 (1)
A Genetic Algorithm for the Design of Minimum-cost Two-connected Networks with Bounded Rings. Ventresca, M.; and Ombuki, B. M. International Journal of Computational Intelligence and Applications, 5(2): 267-281. 2005.
A Genetic Algorithm for the Design of Minimum-cost Two-connected Networks with Bounded Rings. [link]Link   A Genetic Algorithm for the Design of Minimum-cost Two-connected Networks with Bounded Rings. [link]Paper   link   bibtex  
  2004 (1)
Local Search Genetic Algorithms for the Job Shop Scheduling Problem. Ombuki, B. M.; and Ventresca, M. Appl. Intell., 21(1): 99-109. 2004.
Local Search Genetic Algorithms for the Job Shop Scheduling Problem. [link]Link   Local Search Genetic Algorithms for the Job Shop Scheduling Problem. [link]Paper   link   bibtex