A saw-tooth genetic algorithm combining the effects of variable population size and reinitialization to enhance performance

Koumousis, VK; Katsaras, CP

dc.contributor.author	Koumousis, VK	en
dc.contributor.author	Katsaras, CP	en
dc.date.accessioned	2014-03-01T01:23:31Z
dc.date.available	2014-03-01T01:23:31Z
dc.date.issued	2006	en
dc.identifier.issn	1089-778X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/16988
dc.subject	Evolutionary computation	en
dc.subject	Genetic algorithm (GA)	en
dc.subject	Optimization methods	en
dc.subject	Population reinstallation	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.classification	Computer Science, Theory & Methods	en
dc.subject.other	Function evaluation	en
dc.subject.other	Optimization	en
dc.subject.other	Probability	en
dc.subject.other	Random processes	en
dc.subject.other	Statistics	en
dc.subject.other	Population reinstallation	en
dc.subject.other	Population size	en
dc.subject.other	Saw-tooth genetic algorithm	en
dc.subject.other	Genetic algorithms	en
dc.title	A saw-tooth genetic algorithm combining the effects of variable population size and reinitialization to enhance performance	en
heal.type	journalArticle	en
heal.identifier.primary	10.1109/TEVC.2005.860765	en
heal.identifier.secondary	http://dx.doi.org/10.1109/TEVC.2005.860765	en
heal.language	English	en
heal.publicationDate	2006	en
heal.abstract	A genetic algorithm (GA) is proposed that uses a variable population size and periodic partial reinitialization of the population in the form of a saw-tooth function. The aim is to enhance the overall performance of the algorithm relying on the dynamics of evolution of the GA and the synergy of the combined effects of population size variation and reinitialization. Preliminary parametric studies to test the validity of these assertions are performed for two categories of problems, a multimodal function and a unimodal function with different features. The proposed scheme is compared with the conventional GA and micro GA (μGA) of equal computing cost and guidelines for the selection of effective values of the involved parameters are given, which facilitate the implementation of the algorithm. The proposed algorithm is tested for a variety of benchmark problems and a problem generator from which it becomes evident that the saw-tooth scheme enhances the overall performance of GAs. © 2006 IEEE.	en
heal.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC	en
heal.journalName	IEEE Transactions on Evolutionary Computation	en
dc.identifier.doi	10.1109/TEVC.2005.860765	en
dc.identifier.isi	ISI:000235725500002	en
dc.identifier.volume	10	en
dc.identifier.issue	1	en
dc.identifier.spage	19	en
dc.identifier.epage	28	en