Hybrid evolutionary algorithms for large scale continuous problems Export

@inproceedings{citeulike:6056422, abstract = {Evolutionary Algorithms (EAs) are powerful metaheuristics that can be applied to almost any optimization problem. However, different Evolutionary Algorithms own different search capabilities that make them more suitable for one or another optimization problem. Furthermore, the combination of several EAs can boost the performance of individual approaches. In this paper we try to exploit the benefits of the combination of several evolutionary approaches by means of a Hybrid Evolutionary Algorithm, where the participation of each individual algorithm on the overall process is dynamically adjusted through its execution. Two different strategies to perform this adjustment are proposed: one with a constant global population size and another one with variable global population size. Experimental results demonstrate that Hybrid Evolutionary Algorithms outperform the individual ones and that the dynamic strategy with variable population size obtains better results on most of the proposed functions.}, address = {New York, NY, USA}, author = {LaTorre, A. and Pe{\~{n}}a, J. M. and Muelas, S. and Zaforas, M.}, booktitle = {GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation}, citeulike-article-id = {6056422}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1570205}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1569901.1570205}, doi = {10.1145/1569901.1570205}, isbn = {978-1-60558-325-9}, keywords = {continuous\_problems, hybrid}, location = {Montreal, Qu\'{e}bec, Canada}, pages = {1863--1864}, posted-at = {2009-11-02 14:12:58}, priority = {2}, publisher = {ACM}, title = {Hybrid evolutionary algorithms for large scale continuous problems}, url = {http://dx.doi.org/10.1145/1569901.1570205}, year = {2009} }

TY - CONF ID - citeulike:6056422 L3 - citeulike-article-id:6056422 N2 - Evolutionary Algorithms (EAs) are powerful metaheuristics that can be applied to almost any optimization problem. However, different Evolutionary Algorithms own different search capabilities that make them more suitable for one or another optimization problem. Furthermore, the combination of several EAs can boost the performance of individual approaches. In this paper we try to exploit the benefits of the combination of several evolutionary approaches by means of a Hybrid Evolutionary Algorithm, where the participation of each individual algorithm on the overall process is dynamically adjusted through its execution. Two different strategies to perform this adjustment are proposed: one with a constant global population size and another one with variable global population size. Experimental results demonstrate that Hybrid Evolutionary Algorithms outperform the individual ones and that the dynamic strategy with variable population size obtains better results on most of the proposed functions. CY - Montreal, Québec, Canada SN - 978-1-60558-325-9 AD - New York, NY, USA EP - 1864 TI - Hybrid evolutionary algorithms for large scale continuous problems PB - ACM T2 - GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation SP - 1863 KW - continuous_problems KW - hybrid AU - LaTorre, A AU - Pe{ñ}a, JM AU - Muelas, S AU - Zaforas, M PY - 2009/// UR - http://dx.doi.org/10.1145/1569901.1570205 ER -