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|a 9783540692812
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|a Kramer, Oliver
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|a Self-Adaptive Heuristics for Evolutionary Computation
|h Elektronische Ressource
|c by Oliver Kramer
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| 250 |
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|a 1st ed. 2008
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| 260 |
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|a Berlin, Heidelberg
|b Springer Berlin Heidelberg
|c 2008, 2008
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| 300 |
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|a XII, 182 p. 39 illus
|b online resource
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|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
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| 653 |
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|a Engineering mathematics
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|a Computer-Aided Engineering (CAD, CAE) and Design
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|a Artificial Intelligence
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| 653 |
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|a Computer-aided engineering
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| 653 |
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|a Artificial intelligence
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| 653 |
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|a Engineering / Data processing
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| 653 |
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|a Mathematical and Computational Engineering Applications
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|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 490 |
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|a Studies in Computational Intelligence
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| 028 |
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|a 10.1007/978-3-540-69281-2
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| 856 |
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|u https://doi.org/10.1007/978-3-540-69281-2?nosfx=y
|x Verlag
|3 Volltext
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|a 670.285
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| 520 |
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|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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