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dc.contributor.authorde la Maza, Michaelen_US
dc.contributor.authorTidor, Bruceen_US
dc.date.accessioned2004-10-04T14:24:20Z
dc.date.available2004-10-04T14:24:20Z
dc.date.issued1991-12-01en_US
dc.identifier.otherAIM-1345en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/5967
dc.description.abstractModifiable Boltzmann selective pressure is investigated as a tool to control variability in optimizations using genetic algorithms. An implementation of variable selective pressure, modeled after the use of temperature as a parameter in simulated annealing approaches, is described. The convergence behavior of optimization runs is illustrated as a function of selective pressure; the method is compared to a genetic algorithm lacking this control feature and is shown to exhibit superior convergence properties on a small set of test problems. An analysis is presented that compares the selective pressure of this algorithm to a standard selection procedure.en_US
dc.format.extent19 p.en_US
dc.format.extent1678653 bytes
dc.format.extent1307750 bytes
dc.format.mimetypeapplication/postscript
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.relation.ispartofseriesAIM-1345en_US
dc.subjectgenetic algorithmsen_US
dc.subjectsimulated annealingen_US
dc.subjecthybrid searchsstrategiesen_US
dc.subjectfunction optimizationen_US
dc.titleBoltzmannn Weighted Selection Improves Performance of Genetic Algorithmsen_US


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