%0 Book Section %1 noKey %A Bessaou, Mourad %A Pétrowski, Alain %A Siarry, Patrick %B Parallel Problem Solving from Nature PPSN VI %D 2000 %E Schoenauer, Marc %E Deb, Kalyanmoy %E Rudolph, Günther %E Yao, Xin %E Lutton, Evelyne %E Merelo, JuanJulian %E Schwefel, Hans-Paul %I Springer Berlin Heidelberg %K cooperating island masterbib model multimodal speciation %P 437-446 %R 10.1007/3-540-45356-3_43 %T Island Model Cooperating with Speciation for Multimodal Optimization %U http://dx.doi.org/10.1007/3-540-45356-3_43 %V 1917 %X This paper considers a new method that enables a genetic algorithm (GA) to identify and maintain multiple optima of a multimodal function, by creating subpopulations within the niches defined by the multiple optima, thus warranting a good “diversity”. The algorithm is based on a splitting of the traditional GA into a sequence of two processes. Since the GA behavior is determined by the exploration / exploitation balance, during the first step (Exploration), the multipopulation genetic algorithm coupled with a speciation method detects the potential niches by classifying “similar” individuals in the same population. Once the niches are detected. the algorithm achieves an intensification (Exploitation), by allocating a separate portion of the search space to each population. These two steps are alternately performed at a given frequency. Empirical results obtained with F6 Schaffer’s function are then presented to show the reliability of the algorithm. %@ 978-3-540-41056-0

@incollection{noKey, abstract = {This paper considers a new method that enables a genetic algorithm (GA) to identify and maintain multiple optima of a multimodal function, by creating subpopulations within the niches defined by the multiple optima, thus warranting a good “diversity”. The algorithm is based on a splitting of the traditional GA into a sequence of two processes. Since the GA behavior is determined by the exploration / exploitation balance, during the first step (Exploration), the multipopulation genetic algorithm coupled with a speciation method detects the potential niches by classifying “similar” individuals in the same population. Once the niches are detected. the algorithm achieves an intensification (Exploitation), by allocating a separate portion of the search space to each population. These two steps are alternately performed at a given frequency. Empirical results obtained with F6 Schaffer’s function are then presented to show the reliability of the algorithm.}, added-at = {2014-06-11T19:32:21.000+0200}, author = {Bessaou, Mourad and Pétrowski, Alain and Siarry, Patrick}, biburl = {https://www.bibsonomy.org/bibtex/2c5947974745d692c7fc25197df81c142/marcioweck}, booktitle = {Parallel Problem Solving from Nature PPSN VI}, description = {Island Model Cooperating with Speciation for Multimodal Optimization - Springer}, doi = {10.1007/3-540-45356-3_43}, editor = {Schoenauer, Marc and Deb, Kalyanmoy and Rudolph, Günther and Yao, Xin and Lutton, Evelyne and Merelo, JuanJulian and Schwefel, Hans-Paul}, interhash = {a4cbe28dc55c15f507136c0ca57a2825}, intrahash = {c5947974745d692c7fc25197df81c142}, isbn = {978-3-540-41056-0}, keywords = {cooperating island masterbib model multimodal speciation}, language = {English}, pages = {437-446}, publisher = {Springer Berlin Heidelberg}, series = {Lecture Notes in Computer Science}, timestamp = {2014-06-11T19:32:21.000+0200}, title = {Island Model Cooperating with Speciation for Multimodal Optimization}, url = {http://dx.doi.org/10.1007/3-540-45356-3_43}, volume = 1917, year = 2000 }