Overview

Despite decades of work in evolutionary algorithms, there remains a lot of uncertainty as to when it is beneficial or detrimental to use recombination or mutation. This book provides a characterization of the roles that recombination and mutation play in evolutionary algorithms. It integrates prior theoretical work and introduces new theoretical techniques for studying evolutionary algorithms. An aggregation algorithm for Markov chains is introduced which is useful for studying not only evolutionary algorithms specifically, but also complex systems in general. Practical consequences of the theory are explored and a novel method for comparing search and optimization algorithms is introduced. A focus on discrete rather than real-valued representations allows the book to bridge multiple communities, including evolutionary biologists and population geneticists.

Full Product Details

Author:   William M. Spears
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Imprint:   Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Edition:   2000 ed.
Dimensions:   Width: 15.50cm , Height: 1.40cm , Length: 23.50cm
Weight:   1.140kg
ISBN:  

9783540669500

ISBN 10:   3540669507
Pages:   222
Publication Date:   15 June 2000
Audience:   College/higher education ,  Professional and scholarly ,  Undergraduate ,  Postgraduate, Research & Scholarly
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

I. Setting the Stage.- 1. Introduction.- 2. Background.- II. Static Theoretical Analyses.- 3. A Survival Schema Theory for Recombination.- 4. A Construction Schema Theory for Recombination.- 5. Survival and Construction Schema Theory for Recombination.- 6. A Survival Schema Theory for Mutation.- 7. A Construction Schema Theory for Mutation.- 8. Schema Theory: Mutation versus Recombination.- 9. Other Static Characterizations of Mutation and Recombination.- III. Dynamic Theoretical Analyses.- 10. Dynamic Analyses of Mutation and Recombination.- 11. A Dynamic Model of Selection and Mutation.- 12. A Dynamic Model of Selection, Recombination, and Mutation.- 13. An Aggregation Algorithm for Markov Chains.- IV. Empirical Analyses.- 14. Empirical Validation.- V. Summary.- 15. Summary and Discussion.- Appendix: Formal Computations for Aggregation.- References.

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