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dc.contributor.CRUESPUNIVERSIDADE ESTADUAL DE CAMPINASpt_BR
dc.contributor.authorunicampFrança, Paulo Morelato-
dc.typeArtigopt_BR
dc.titleGlobal optimization using a genetic algorithm with hierarchically structured populationpt_BR
dc.contributor.authorToledo, C. F. M.-
dc.contributor.authorOliveira, L.-
dc.contributor.authorFranca, P. M.-
dc.subjectAlgoritmos genéticospt_BR
dc.subjectOtimização globalpt_BR
dc.subject.otherlanguageGenetic algorithmspt_BR
dc.subject.otherlanguageGlobal optimizationpt_BR
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 accountpt_BR
dc.relation.ispartofJournal of computational and applied mathematicspt_BR
dc.publisher.cityAmsterdampt_BR
dc.publisher.countryPaíses Baixospt_BR
dc.publisherElsevierpt_BR
dc.date.issued2014-
dc.date.monthofcirculationMaypt_BR
dc.language.isoengpt_BR
dc.description.volume261pt_BR
dc.description.firstpage341pt_BR
dc.description.lastpage351pt_BR
dc.rightsAbertopt_BR
dc.sourceWOSpt_BR
dc.identifier.issn0377-0427pt_BR
dc.identifier.eissn1879-1778pt_BR
dc.identifier.doi10.1016/j.cam.2013.11.008pt_BR
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S0377042713006274pt_BR
dc.date.available2020-11-11T14:58:14Z-
dc.date.accessioned2020-11-11T14:58:14Z-
dc.description.provenanceSubmitted by Cintia Oliveira de Moura (cintiaom@unicamp.br) on 2020-11-11T14:58:14Z No. of bitstreams: 0. Added 1 bitstream(s) on 2021-02-16T16:52:54Z : No. of bitstreams: 1 000331507900028.pdf: 1268790 bytes, checksum: eebdff4a60a20924bf840b14388e3866 (MD5)en
dc.description.provenanceMade available in DSpace on 2020-11-11T14:58:14Z (GMT). No. of bitstreams: 0 Previous issue date: 2014en
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/352166-
dc.contributor.departmentDepartamento de Sistemas e Energiapt_BR
dc.contributor.unidadeFaculdade de Engenharia Elétrica e da Computaçãopt_BR
dc.identifier.source000331507900028pt_BR
dc.type.formArtigopt_BR
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