Self-Adaptive Heuristics for Evolutionary Computation
(Sprache: Englisch)
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...
Leider schon ausverkauft
versandkostenfrei
Buch (Gebunden)
106.99 €
Produktdetails
Produktinformationen zu „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-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.
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.
Klappentext zu „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-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. TOC:Part I Foundations of Evolutionary Computation.- Evolutionary Algorithms.- Self-Adaptation.- Part II Self-Adaptive Operators.- Biased Mutation for Evolution Strategies.- Self-Adaptive Inversion Mutation.- Self-Adaptive Crossover.- Part III Constraint Handling.- Constraint Handling Heuristics for Evolution Strategies.
Inhaltsverzeichnis zu „Self-Adaptive Heuristics for Evolutionary Computation “
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.
Bibliographische Angaben
- Autor: Oliver Kramer
- 2008, 2008, 196 Seiten, Maße: 16 x 24,1 cm, Gebunden, Englisch
- Verlag: Springer Berlin Heidelberg
- ISBN-10: 3540692800
- ISBN-13: 9783540692805
- Erscheinungsdatum: 19.08.2008
Sprache:
Englisch
Kommentar zu "Self-Adaptive Heuristics for Evolutionary Computation"
0 Gebrauchte Artikel zu „Self-Adaptive Heuristics for Evolutionary Computation“
Zustand | Preis | Porto | Zahlung | Verkäufer | Rating |
---|
Schreiben Sie einen Kommentar zu "Self-Adaptive Heuristics for Evolutionary Computation".
Kommentar verfassen