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Self-Adaptive Heuristics for Evolutionary Computation
Evolutionary algorithms are successful biologically inspired meta-heuristics. Their success depends on adequate parameter settings. The question arises: how can evolutionary algorithms learn parameters automatically during the optimization? Evolution strategies gave an answer decades ago: self-adapt...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2008.
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| Edición: | 1st ed. 2008. |
| Colección: | Studies in Computational Intelligence,
147 |
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 024 | 7 | |a 10.1007/978-3-540-69281-2 |2 doi | |
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| 100 | 1 | |a Kramer, Oliver. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Self-Adaptive Heuristics for Evolutionary Computation |h [electronic resource] / |c by Oliver Kramer. |
| 250 | |a 1st ed. 2008. | ||
| 264 | 1 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg : |b Imprint: Springer, |c 2008. | |
| 300 | |a XII, 182 p. 39 illus. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 490 | 1 | |a Studies in Computational Intelligence, |x 1860-9503 ; |v 147 | |
| 505 | 0 | |a I: Foundations of Evolutionary Computation -- Evolutionary Algorithms -- Self-Adaptation -- II: Self-Adaptive Operators -- Biased Mutation for Evolution Strategies -- Self-Adaptive Inversion Mutation -- Self-Adaptive Crossover -- III: Constraint Handling -- Constraint Handling Heuristics for Evolution Strategies -- IV: Summary -- Summary and Conclusion -- V: Appendix -- Continuous Benchmark Functions -- Discrete Benchmark Functions. | |
| 520 | |a Evolutionary algorithms are successful biologically inspired meta-heuristics. Their success depends on adequate parameter settings. The question arises: how can evolutionary algorithms learn parameters automatically during the optimization? Evolution strategies gave an answer decades ago: self-adaptation. Their self-adaptive mutation control turned out to be exceptionally successful. But nevertheless self-adaptation has not achieved the attention it deserves. This book introduces various types of self-adaptive parameters for evolutionary computation. Biased mutation for evolution strategies is useful for constrained search spaces. Self-adaptive inversion mutation accelerates the search on combinatorial TSP-like problems. After the analysis of self-adaptive crossover operators the book concentrates on premature convergence of self-adaptive mutation control at the constraint boundary. Besides extensive experiments, statistical tests and some theoretical investigations enrich the analysis of the proposed concepts. | ||
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| 650 | 2 | 4 | |a Artificial Intelligence. |
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| 776 | 0 | 8 | |i Printed edition: |z 9783642088780 |
| 776 | 0 | 8 | |i Printed edition: |z 9783540865179 |
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| 830 | 0 | |a Studies in Computational Intelligence, |x 1860-9503 ; |v 147 | |
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