Self-Adaptive Heuristics for Evolutionary Computation 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024

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Kramer, Oliver 作者

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194 页数

$ 179.67 价格

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9783540692805 图书编码

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发表于2024-11-24

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Self-Adaptive Heuristics for Evolutionary Computation 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

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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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