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About 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.
Detailed Information
| Author: | Oliver Kramer (auth.) |
|---|---|
| Publication Year: | 2008 |
| ISBN: | 9783540692805 |
| Pages: | 178 |
| Language: | English |
| File Size: | 4.37 |
| Format: | |
| Price: | FREE |
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