Boltzmannn Weighted Selection Improves Performance of Genetic Algorithms

dc.creator	de la Maza, Michael
dc.creator	Tidor, Bruce
dc.date	2004-10-04T14:24:20Z
dc.date	2004-10-04T14:24:20Z
dc.date	1991-12-01
dc.date.accessioned	2013-10-09T02:42:09Z
dc.date.available	2013-10-09T02:42:09Z
dc.date.issued	2013-10-09
dc.identifier	AIM-1345
dc.identifier	http://hdl.handle.net/1721.1/5967
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721
dc.description	Modifiable 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.
dc.format	19 p.
dc.format	1678653 bytes
dc.format	1307750 bytes
dc.format	application/postscript
dc.format	application/pdf
dc.language	en_US
dc.relation	AIM-1345
dc.subject	genetic algorithms
dc.subject	simulated annealing
dc.subject	hybrid searchsstrategies
dc.subject	function optimization
dc.title	Boltzmannn Weighted Selection Improves Performance of Genetic Algorithms

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