Self-Adaptive Heuristics for Evolutionary Computation (inbunden)
Format
Inbunden (Hardback)
Språk
Engelska
Antal sidor
182
Utgivningsdatum
2008-08-03
Upplaga
2008 ed.
Förlag
Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Illustratör/Fotograf
37 schwarz-weiße Zeichnungen 39 schwarz-weiße Abbildungen, 2 schwarz-weiße Fotos 38 schwarz-weiße
Illustrationer
38 Tables, black and white; 39 Illustrations, black and white; XII, 182 p. 39 illus.
Dimensioner
239 x 157 x 15 mm
Vikt
409 g
Antal komponenter
1
Komponenter
1 Hardback
ISBN
9783540692805
Self-Adaptive Heuristics for Evolutionary Computation (inbunden)

Self-Adaptive Heuristics for Evolutionary Computation

Inbunden Engelska, 2008-08-03
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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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Innehållsförteckning

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.