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. Science, 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   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   bibtex
  2015 (3)
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   bibtex
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   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   bibtex
  2014 (6)
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   bibtex   buy
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   bibtex
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   bibtex
Genetic Programming for the Automatic Inference of Graph Models for Complex Networks. Bailey, A.; Ventresca, M.; and Ombuki-Berman, B. M. IEEE Trans. Evolutionary Computation, 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   bibtex
A randomized algorithm with local search for containment of pandemic disease spread. Ventresca, M.; and Aleman, D. M. Computers & OR, 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   bibtex
A derandomized approximation algorithm for the critical node detection problem. Ventresca, M.; and Aleman, D. M. Computers & OR, 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   bibtex
  2013 (3)
Evaluation of strategies to mitigate contagion spread using social network characteristics. Ventresca, M.; and Aleman, D. M. Social 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   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   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   bibtex
  2012 (3)
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   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   bibtex
Global search algorithms using a combinatorial unranking-based problem representation for the critical node detection problem. Ventresca, M. Computers & OR, 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   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   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   bibtex
  2009 (3)
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   bibtex
Improving gradient-based learning algorithms for large scale feedforward networks. Ventresca, M.; and Tizhoosh, H. R. In IJCNN, pages 3212-3219, 2009. IEEE
Improving gradient-based learning algorithms for large scale feedforward networks. [link]Link   Improving gradient-based learning algorithms for large scale feedforward networks. [link]Paper   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   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   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   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   bibtex   buy
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   bibtex   buy
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   bibtex   buy
  2007 (4)
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   bibtex
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   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   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   bibtex
  2006 (4)
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   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   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   bibtex
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   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   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   bibtex