Self-Adaptive Heuristics for Evolutionary Computation
Springer Berlin (Verlag)
978-3-540-69280-5 (ISBN)
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.
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.
| Erscheint lt. Verlag | 19.8.2008 |
|---|---|
| Reihe/Serie | Studies in Computational Intelligence |
| Zusatzinfo | XII, 182 p. 39 illus. |
| Verlagsort | Berlin |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 460 g |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Informatik ► Weitere Themen ► CAD-Programme | |
| Mathematik / Informatik ► Mathematik ► Angewandte Mathematik | |
| Technik | |
| Schlagworte | algorithm • algorithms • Biologically Inspired • Computational Intelligence • Computer-Aided Design (CAD) • Evolution • evolutionary algorithm • evolutionary computation • Evolutionary Intelligence • Heuristics • Metaheuristic • Mutation • Operator • Optimization • Self-Adaptive Heuristics |
| ISBN-10 | 3-540-69280-0 / 3540692800 |
| ISBN-13 | 978-3-540-69280-5 / 9783540692805 |
| Zustand | Neuware |
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