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http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/5967| Title: | Boltzmannn Weighted Selection Improves Performance of Genetic Algorithms |
| Keywords: | genetic algorithms simulated annealing hybrid searchsstrategies function optimization |
| Issue Date: | 9-Oct-2013 |
| 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. |
| URI: | http://koha.mediu.edu.my:8181/xmlui/handle/1721 |
| Other Identifiers: | AIM-1345 http://hdl.handle.net/1721.1/5967 |
| Appears in Collections: | MIT Items |
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