Global optimization using a genetic algorithm with hierarchically structured population

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dc.contributor.authorToledo, C. F. M.
dc.contributor.authorOliveira, L.
dc.contributor.authorFranca, P. M. [UNESP]
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.contributor.institutionUniversidade Estadual de Campinas (UNICAMP)
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2014-12-03T13:08:56Z
dc.date.available2014-12-03T13:08:56Z
dc.date.issued2014-05-01
dc.description.abstractThis paper applies a genetic algorithm with hierarchically structured population to solve unconstrained optimization problems. The population has individuals distributed in several overlapping clusters, each one with a leader and a variable number of support individuals. The hierarchy establishes that leaders must be fitter than its supporters with the topological organization of the clusters following a tree. Computational tests evaluate different population structures, population sizes and crossover operators for better algorithm performance. A set of known benchmark test problems is solved and the results found are compared with those obtained from other methods described in the literature, namely, two genetic algorithms, a simulated annealing, a differential evolution and a particle swarm optimization. The results indicate that the method employed is capable of achieving better performance than the previous approaches in regard as the two criteria usually employed for comparisons: the number of function evaluations and rate of success. The method also has a superior performance if the number of problems solved is taken into account. (C) 2013 Elsevier B.V. All rights reserved.en
dc.description.affiliationUniv Sao Paulo, ICMC, BR-13566590 Sao Carlos, SP, Brazil
dc.description.affiliationUniv Estadual Campinas, Inst Comp, BR-13083970 Campinas, SP, Brazil
dc.description.affiliationUniv Estadual Paulista, BR-19060900 Presidente Prudente, SP, Brazil
dc.description.affiliationUnespUniv Estadual Paulista, BR-19060900 Presidente Prudente, SP, Brazil
dc.format.extent341-351
dc.identifierhttp://dx.doi.org/10.1016/j.cam.2013.11.008
dc.identifier.citationJournal Of Computational And Applied Mathematics. Amsterdam: Elsevier Science Bv, v. 261, p. 341-351, 2014.
dc.identifier.doi10.1016/j.cam.2013.11.008
dc.identifier.issn0377-0427
dc.identifier.urihttp://hdl.handle.net/11449/111731
dc.identifier.wosWOS:000331507900028
dc.language.isoeng
dc.publisherElsevier B.V.
dc.relation.ispartofJournal of Computational and Applied Mathematics
dc.relation.ispartofjcr1.632
dc.relation.ispartofsjr0,938
dc.rights.accessRightsAcesso restrito
dc.sourceWeb of Science
dc.subjectGenetic algorithmsen
dc.subjectGlobal optimizationen
dc.subjectContinuous optimizationen
dc.subjectPopulation set-based methodsen
dc.subjectHierarchical structureen
dc.titleGlobal optimization using a genetic algorithm with hierarchically structured populationen
dc.typeArtigo
dcterms.licensehttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dcterms.rightsHolderElsevier B.V.
unesp.author.orcid0000-0002-0490-5515[3]
unesp.author.orcid0000-0003-4776-8052[1]

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