Description: Self-Adaptive Heuristics for Evolutionary Computation Please note: this item is printed on demand and will take extra time before it can be dispatched to you (up to 20 working days). Author(s): Oliver Kramer Format: Paperback Publisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Germany Imprint: Springer-Verlag Berlin and Heidelberg GmbH & Co. K ISBN-13: 9783642088780, 978-3642088780 Synopsis 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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Book Title: Self-Adaptive Heuristics for Evolutionary Computation
Number of Pages: 182 Pages
Language: English
Publication Name: Self-Adaptive Heuristics for Evolutionary Computation
Publisher: Springer-Verlag Berlin AND Heidelberg Gmbh & Co. KG
Publication Year: 2010
Subject: Engineering & Technology, Computer Science
Item Height: 235 mm
Item Weight: 308 g
Type: Textbook
Author: Oliver Kramer
Series: Studies in Computational Intelligence
Item Width: 155 mm
Format: Paperback
