Genetic algorithms in competitive environments

Dimou, CK; Koumousis, VK

dc.contributor.author	Dimou, CK	en
dc.contributor.author	Koumousis, VK	en
dc.date.accessioned	2014-03-01T01:19:01Z
dc.date.available	2014-03-01T01:19:01Z
dc.date.issued	2003	en
dc.identifier.issn	0887-3801	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/15322
dc.subject	Algorithms	en
dc.subject	Optimization	en
dc.subject	Reliability analysis	en
dc.subject	Structural analysis	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.classification	Engineering, Civil	en
dc.subject.other	Competition	en
dc.subject.other	Computational methods	en
dc.subject.other	Fuzzy control	en
dc.subject.other	Optimal control systems	en
dc.subject.other	Optimization	en
dc.subject.other	Parameter estimation	en
dc.subject.other	Population statistics	en
dc.subject.other	Probability	en
dc.subject.other	Problem solving	en
dc.subject.other	Statistical methods	en
dc.subject.other	Structural analysis	en
dc.subject.other	Computational efficiency	en
dc.subject.other	Optimal design	en
dc.subject.other	Parameter control	en
dc.subject.other	Reliability analysis	en
dc.subject.other	Genetic algorithms	en
dc.title	Genetic algorithms in competitive environments	en
heal.type	journalArticle	en
heal.identifier.primary	10.1061/(ASCE)0887-3801(2003)17:3(142)	en
heal.identifier.secondary	http://dx.doi.org/10.1061/(ASCE)0887-3801(2003)17:3(142)	en
heal.language	English	en
heal.publicationDate	2003	en
heal.abstract	Competition is introduced among the populations of a number of genetic algorithms (GAs) in solving optimization problems. The aim is to adapt the parameters of the GAs, by altering the resources of the system, so as to achieve better solutions. The evolution of the different populations, having different sets of parameters, is controlled at the level of metapopulation, i.e., the union of populations, on the basis of statistics and trends of the evolution of every population. An overall fitness measure is introduced that incorporates a diversity measure and the required resources to rank the populations. The fuzzy outcome of the conflict among the populations guides the evolution of the different GAs toward better solutions in the statistical sense. The proposed scheme is applied to two different problems - a multimodal function with six global and several near-global optima, and a reliability based optimal design of a simple truss. Numerical results are presented, and the robustness and computational efficiency of the proposed scheme are discussed. © ASCE,.	en
heal.publisher	ASCE-AMER SOC CIVIL ENGINEERS	en
heal.journalName	Journal of Computing in Civil Engineering	en
dc.identifier.doi	10.1061/(ASCE)0887-3801(2003)17:3(142)	en
dc.identifier.isi	ISI:000183651600003	en
dc.identifier.volume	17	en
dc.identifier.issue	3	en
dc.identifier.spage	142	en
dc.identifier.epage	149	en