Ebook: Evolutionary Algorithms: The Role of Mutation and Recombination

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.

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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.

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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.
Content:
Front Matter....Pages I-XIV
Front Matter....Pages 1-1
Introduction....Pages 3-18
Background....Pages 19-35
Front Matter....Pages 37-37
A Survival Schema Theory for Recombination....Pages 39-58
A Construction Schema Theory for Recombination....Pages 59-75
Survival and Construction Schema Theory for Recombination....Pages 77-82
A Survival Schema Theory for Mutation....Pages 83-90
A Construction Schema Theory for Mutation....Pages 91-100
Schema Theory: Mutation versus Recombination....Pages 101-115
Other Static Characterizations of Mutation and Recombination....Pages 117-126
Front Matter....Pages 127-127
Dynamic Analyses of Mutation and Recombination....Pages 129-146
A Dynamic Model of Selection and Mutation....Pages 147-153
A Dynamic Model of Selection, Recombination, and Mutation....Pages 155-168
An Aggregation Algorithm for Markov Chains....Pages 169-190
Front Matter....Pages 191-191
Empirical Validation....Pages 193-201
Front Matter....Pages 203-203
Summary and Discussion....Pages 205-210
Back Matter....Pages 211-222

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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.
Content:
Front Matter....Pages I-XIV
Front Matter....Pages 1-1
Introduction....Pages 3-18
Background....Pages 19-35
Front Matter....Pages 37-37
A Survival Schema Theory for Recombination....Pages 39-58
A Construction Schema Theory for Recombination....Pages 59-75
Survival and Construction Schema Theory for Recombination....Pages 77-82
A Survival Schema Theory for Mutation....Pages 83-90
A Construction Schema Theory for Mutation....Pages 91-100
Schema Theory: Mutation versus Recombination....Pages 101-115
Other Static Characterizations of Mutation and Recombination....Pages 117-126
Front Matter....Pages 127-127
Dynamic Analyses of Mutation and Recombination....Pages 129-146
A Dynamic Model of Selection and Mutation....Pages 147-153
A Dynamic Model of Selection, Recombination, and Mutation....Pages 155-168
An Aggregation Algorithm for Markov Chains....Pages 169-190
Front Matter....Pages 191-191
Empirical Validation....Pages 193-201
Front Matter....Pages 203-203
Summary and Discussion....Pages 205-210
Back Matter....Pages 211-222
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