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...
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| Главный автор: | |
|---|---|
| Формат: | Livre numérique |
| Язык: | Anglais |
| Опубликовано: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
2008.
Cham : Springer Nature |
| Серии: | Studies in Computational Intelligence
147 |
| Online-ссылка: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Примечание: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Self-Adaptive Heuristics for Evolutionary Computation, Texte imprimé, 9783642088780 • Self-Adaptive Heuristics for Evolutionary Computation, Texte imprimé, 9783540865179 • Self-Adaptive Heuristics for Evolutionary Computation, Texte imprimé, 9783540692805 |
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| 100 | 1 | |a Kramer, Oliver. | |
| 245 | 1 | 0 | |a Self-Adaptive Heuristics for Evolutionary Computation |c by Oliver Kramer ; edited by Janusz Kacprzyk. |
| 260 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg. | ||
| 260 | |a Cham : |b Springer Nature, |c 2008. | ||
| 490 | 0 | |a Studies in Computational Intelligence |v 147 |x 1860-9503 | |
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 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. | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. chttps://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 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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| 776 | 0 | |t Self-Adaptive Heuristics for Evolutionary Computation |b Texte imprimé |z 9783540865179 | |
| 776 | 0 | |t Self-Adaptive Heuristics for Evolutionary Computation |b Texte imprimé |z 9783540692805 | |
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