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...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Livre numérique |
| Sprache: | Anglais |
| Veröffentlicht: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
2008.
Cham : Springer Nature |
| Schriftenreihe: | Studies in Computational Intelligence
147 |
| Online Zugang: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Anmerkung: |
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 |
Inhaltsangabe:
- 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.