
Title

SelfAdaptive Heuristics for Evolutionary Computation (Studies in Computational Intelligence, 147),New
Delivery time: 8-12 business days (International)
Evolutionary algorithms are successful biologically inspired metaheuristics. 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: selfadaptation. Their selfadaptive mutation control turned out to be exceptionally successful. But nevertheless selfadaptation has not achieved the attention it deserves.This book introduces various types of selfadaptive parameters for evolutionary computation. Biased mutation for evolution strategies is useful for constrained search spaces. Selfadaptive inversion mutation accelerates the search on combinatorial TSPlike problems. After the analysis of selfadaptive crossover operators the book concentrates on premature convergence of selfadaptive 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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We provide a 2-year limited warranty, from the date of purchase for all our products.
If you believe you have received a defective product, or are experiencing any problems with your product, please contact us.
This warranty strictly does not cover damages that arose from negligence, misuse, wear and tear, or not in accordance with product instructions (dropping the product, etc.).
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