Global optimization using a genetic algorithm with hierarchically structured population

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorToledo, C. F. M.-
Autor(es): dc.creatorOliveira, L.-
Autor(es): dc.creatorFranca, P. M.-
Data de aceite: dc.date.accessioned2021-03-10T21:18:00Z-
Data de disponibilização: dc.date.available2021-03-10T21:18:00Z-
Data de envio: dc.date.issued2014-12-03-
Data de envio: dc.date.issued2014-12-03-
Data de envio: dc.date.issued2014-05-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.cam.2013.11.008-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/111731-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/111731-
Descrição: dc.descriptionThis 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.-
Formato: dc.format341-351-
Idioma: dc.languageen-
Publicador: dc.publisherElsevier B.V.-
Relação: dc.relationJournal of Computational and Applied Mathematics-
Relação: dc.relation1.632-
Relação: dc.relation0,938-
Direitos: dc.rightsclosedAccess-
Palavras-chave: dc.subjectGenetic algorithms-
Palavras-chave: dc.subjectGlobal optimization-
Palavras-chave: dc.subjectContinuous optimization-
Palavras-chave: dc.subjectPopulation set-based methods-
Palavras-chave: dc.subjectHierarchical structure-
Título: dc.titleGlobal optimization using a genetic algorithm with hierarchically structured population-
Tipo de arquivo: dc.typelivro digital-
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