%0 Journal Article %1 5291795 %A Yao, Jie %A Kharma, N. %A Grogono, P. %D 2010 %J Evolutionary Computation, IEEE Transactions on %K algorithm bi genetic masterbib multimodal multipopulation objective %N 1 %P 80-102 %R 10.1109/TEVC.2009.2017517 %T Bi-Objective Multipopulation Genetic Algorithm for Multimodal Function Optimization %U http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5291795&tag=1 %V 14 %X This paper describes the latest version of a bi-objective multipopulation genetic algorithm (BMPGA) aiming to locate all global and local optima on a real-valued differentiable multimodal landscape. The performance of BMPGA is compared against four multimodal GAs on five multimodal functions. BMPGA is distinguished by its use of two separate but complementary fitness objectives designed to enhance the diversity of the overall population and exploration of the search space. This is coupled with a multipopulation and clustering scheme, which focuses selection within the various sub-populations and results in effective identification and retention of the optima of the target functions as well as improved exploitation within promising areas. The results of the empirical comparison provide clear evidence that supports the conclusion that BMPGA is better than the other GAs in terms of overall effectiveness, applicability, and reliability. The practical value of BMPGA has already been demonstrated in applications to multiple ellipses and elliptic objects detection in microscopic imagery.

@article{5291795, abstract = {This paper describes the latest version of a bi-objective multipopulation genetic algorithm (BMPGA) aiming to locate all global and local optima on a real-valued differentiable multimodal landscape. The performance of BMPGA is compared against four multimodal GAs on five multimodal functions. BMPGA is distinguished by its use of two separate but complementary fitness objectives designed to enhance the diversity of the overall population and exploration of the search space. This is coupled with a multipopulation and clustering scheme, which focuses selection within the various sub-populations and results in effective identification and retention of the optima of the target functions as well as improved exploitation within promising areas. The results of the empirical comparison provide clear evidence that supports the conclusion that BMPGA is better than the other GAs in terms of overall effectiveness, applicability, and reliability. The practical value of BMPGA has already been demonstrated in applications to multiple ellipses and elliptic objects detection in microscopic imagery.}, added-at = {2014-06-11T19:07:30.000+0200}, author = {Yao, Jie and Kharma, N. and Grogono, P.}, biburl = {https://www.bibsonomy.org/bibtex/283ec7ff55d42f89b57f91ccbcccd6ef4/marcioweck}, description = {IEEE Xplore Abstract - Bi-Objective Multipopulation Genetic Algorithm for Multimodal Function Optimization}, doi = {10.1109/TEVC.2009.2017517}, interhash = {41ce627de6e02327cadb1cda805b870c}, intrahash = {83ec7ff55d42f89b57f91ccbcccd6ef4}, issn = {1089-778X}, journal = {Evolutionary Computation, IEEE Transactions on}, keywords = {algorithm bi genetic masterbib multimodal multipopulation objective}, month = feb, number = 1, pages = {80-102}, timestamp = {2014-06-11T19:07:30.000+0200}, title = {Bi-Objective Multipopulation Genetic Algorithm for Multimodal Function Optimization}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5291795&tag=1}, volume = 14, year = 2010 }