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dc.contributor.authorBoyabatlı, O.en_US
dc.contributor.authorSabuncuoglu, I.en_US
dc.date.accessioned2019-02-12T06:19:41Z
dc.date.available2019-02-12T06:19:41Z
dc.date.issued2004en_US
dc.identifier.issn1690-4532
dc.identifier.urihttp://hdl.handle.net/11693/49280
dc.description.abstractIn this study, we provide a new taxonomy of parameters of genetic algorithms (GA), structural and numerical parameters, and analyze the effect of numerical parameters on the performance of GA based simulation optimization applications with experimental design techniques. Appropriate levels of each parameter are proposed for a particular problem domain. Controversial to existing literature on GA, our computational results reveal that in the case of a dominant set of decision variable the crossover operator does not have a significant impact on the performance measures, whereas high mutation rates are more suitable for GA applications.en_US
dc.language.isoEnglishen_US
dc.source.titleJournal of Systemics, Cybernetics and Informaticsen_US
dc.subjectSimulationen_US
dc.subjectOptimizationen_US
dc.subjectGenetic Algorithmen_US
dc.subjectParameter selectionen_US
dc.subjectFactorial designen_US
dc.titleParameter selection in genetic algorithmsen_US
dc.typeArticleen_US
dc.departmentDepartment of Industrial Engineeringen_US
dc.citation.spage78en_US
dc.citation.epage83en_US
dc.citation.volumeNumber4en_US
dc.citation.issueNumber2en_US
dc.publisherInternational Institute of Informatics and Cyberneticsen_US
dc.identifier.eissn1690-4524


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