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.
Glossary.- Part I. Setting the Stage: Introduction.- Background.- Part II. Static Theoretical Analyses: A Survival Schema Theory for Recombination.- A Construction Schema Theory for Recombination.- Survival and Construction Schema Theory for Recombination.- A Survival Schema Theory for Mutation.- A Construction Schema Theory for Mutation.- Schema Theory: Mutation versus Recombination.- Other Static Characterizations of Mutation and Recombination.- Part III. Dynamic Theoretical Analyses: Dynamic Analyses of Mutation and Recombination.- A Dynamic Model of Selection and Mutation.- A Dynamic Model of Selection, Recombination and Mutation.- An Aggregation Algorithm for Markov Chains.- Part IV. Empirical Analyses: Empirical Validation.- Part V. Summary: Summary and Discussion.- Appendix: Formal Computations for the Aggregation Algorithm.- Bibliography.
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