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Self-Adaptive Heuristics for Evolutionary Computation

by Oliver Kramer
Save 35% Save 35%
Current price ₹7,345.00
Original price ₹11,299.00
Original price ₹11,299.00
Original price ₹11,299.00
(-35%)
₹7,345.00
Current price ₹7,345.00

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Book cover type: Paperback
  • ISBN13: 9783642088780
  • Binding: Paperback
  • Subject: N/A
  • Publisher: Springer
  • Publisher Imprint: Springer
  • Publication Date:
  • Pages: 182
  • Original Price: EUR 99.99
  • Language: English
  • Edition: N/A
  • Item Weight: 304 grams
  • BISAC Subject(s): Applied, Artificial Intelligence / General, and Design, Graphics & Media / CAD-CAM

From the Back Cover

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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