Boltzmannn Weighted Selection Improves Performance of Genetic Algorithms
dc.date.accessioned | 2004-10-04T14:24:20Z | |
dc.date.accessioned | 2018-11-24T10:11:19Z | |
dc.date.available | 2004-10-04T14:24:20Z | |
dc.date.available | 2018-11-24T10:11:19Z | |
dc.date.issued | 1991-12-01 | en_US |
dc.identifier.uri | http://hdl.handle.net/1721.1/5967 | |
dc.identifier.uri | http://repository.aust.edu.ng/xmlui/handle/1721.1/5967 | |
dc.description.abstract | 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. | en_US |
dc.format.extent | 19 p. | en_US |
dc.format.extent | 1678653 bytes | |
dc.format.extent | 1307750 bytes | |
dc.language.iso | en_US | |
dc.subject | genetic algorithms | en_US |
dc.subject | simulated annealing | en_US |
dc.subject | hybrid searchsstrategies | en_US |
dc.subject | function optimization | en_US |
dc.title | Boltzmannn Weighted Selection Improves Performance of Genetic Algorithms | en_US |
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