The effects of two new crossover operators on genetic algorithm performance

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dc.contributor.authorKaya, Mustafa
dc.date.accessioned13.07.201910:50:10
dc.date.accessioned2019-07-29T19:26:23Z
dc.date.available13.07.201910:50:10
dc.date.available2019-07-29T19:26:23Z
dc.date.issued2011
dc.departmentMühendislik Fakültesi
dc.description.abstractIn this study, two new crossover operators in genetic algorithm are proposed; sequential and random mixed crossover. In the first stage, existing and developed crossover operators were applied to two benchmark problems, namely the reinforced concrete beam problem and the space truss problem. In the second stage, the developed crossover operators were applied to a deep beam problem and, a concrete mix design problem. The fittest values obtained using developed crossover operators were higher than those were obtained with existing crossover operator after 30,000 generations. Moreover, in developed crossover operators, the random mixed crossover yields higher fitness values than the existing crossover operators. (c) 2010 Elsevier B. V. All rights reserved.
dc.identifier.doi10.1016/j.asoc.2010.01.008
dc.identifier.endpage890en_US
dc.identifier.issn1568-4946
dc.identifier.issue1en_US
dc.identifier.scopusqualityQ1
dc.identifier.startpage881en_US
dc.identifier.urihttps://doi.org/10.1016/j.asoc.2010.01.008
dc.identifier.urihttps://hdl.handle.net/20.500.12451/5571
dc.identifier.volume11en_US
dc.identifier.wosWOS:000281591300087
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Science Bv
dc.relation.ispartofApplied Soft Computing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectGenetic Algorithm
dc.subjectCrossover Operators
dc.titleThe effects of two new crossover operators on genetic algorithm performance
dc.typeArticle

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