Global optimization using a genetic algorithm with hierarchically structured population

dc.creatorToledo, CFM
dc.creatorOliveira, L
dc.creatorFranca, PM
dc.date2014
dc.dateMAY 1
dc.date2014-07-30T17:48:02Z
dc.date2015-11-26T17:48:19Z
dc.date2014-07-30T17:48:02Z
dc.date2015-11-26T17:48:19Z
dc.date.accessioned2018-03-29T00:31:08Z
dc.date.available2018-03-29T00:31:08Z
dc.identifierJournal Of Computational And Applied Mathematics. Elsevier Science Bv, v. 261, n. 341, n. 351, 2014.
dc.identifier0377-0427
dc.identifier1879-1778
dc.identifierWOS:000331507900028
dc.identifier10.1016/j.cam.2013.11.008
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/68087
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/68087
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1289052
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.
dc.description261
dc.description341
dc.description351
dc.languageen
dc.publisherElsevier Science Bv
dc.publisherAmsterdam
dc.publisherHolanda
dc.relationJournal Of Computational And Applied Mathematics
dc.relationJ. Comput. Appl. Math.
dc.rightsfechado
dc.rightshttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dc.sourceWeb of Science
dc.subjectGenetic algorithms
dc.subjectGlobal optimization
dc.subjectContinuous optimization
dc.subjectPopulation set-based methods
dc.subjectHierarchical structure
dc.subjectMemetic Algorithm
dc.subjectParticle Swarm
dc.subjectMultimodal Functions
dc.subjectScheduling Problem
dc.titleGlobal optimization using a genetic algorithm with hierarchically structured population
dc.typeArtículos de revistas


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