Self-Adaptive Heuristics for Evolutionary Computation (2008) (Studies in Computational Intelligence #147)
By:
Sign Up Now!
Already a Member? Log In
You must be logged into UK education collection to access this title.
Learn about membership options,
or view our freely available titles.
- 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.
- Copyright:
- 2008
Book Details
- Book Quality:
- Publisher Quality
- ISBN-13:
- 9783540692812
- Related ISBNs:
- 9783540692805
- Publisher:
- Springer Berlin Heidelberg
- Date of Addition:
- 07/21/22
- Copyrighted By:
- N/A
- Adult content:
- No
- Language:
- English
- Has Image Descriptions:
- No
- Categories:
- Nonfiction, Computers and Internet, Technology
- Submitted By:
- Bookshare Staff
- Usage Restrictions:
- This is a copyrighted book.