Ebook: Representations for Genetic and Evolutionary Algorithms

In the field of genetic and evolutionary algorithms (GEAs), a large amount of theory and empirical study has focused on operators and test problems, while problem representation has often been taken as given. This book breaks away from this tradition and provides a comprehensive overview on the influence of problem representations on GEA performance.
The book summarizes existing knowledge regarding problem representations and describes how basic properties of representations, such as redundancy, scaling, or locality, influence the performance of GEAs and other heuristic optimization methods. Using the developed theory, representations can be analyzed and designed in a theory-guided matter. The theoretical concepts are used for solving integer optimization problems and network design problems more efficiently.
The book is written in an easy-to-read style and is intended for researchers, practitioners, and students who want to learn about representations. This second edition extends the analysis of the basic properties of representations and introduces a new chapter on the analysis of direct representations.

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In the field of genetic and evolutionary algorithms (GEAs), a large amount of theory and empirical study has focused on operators and test problems, while problem representation has often been taken as given. This book breaks away from this tradition and provides a comprehensive overview on the influence of problem representations on GEA performance.
The book summarizes existing knowledge regarding problem representations and describes how basic properties of representations, such as redundancy, scaling, or locality, influence the performance of GEAs and other heuristic optimization methods. Using the developed theory, representations can be analyzed and designed in a theory-guided matter. The theoretical concepts are used for solving integer optimization problems and network design problems more efficiently.
The book is written in an easy-to-read style and is intended for researchers, practitioners, and students who want to learn about representations. This second edition extends the analysis of the basic properties of representations and introduces a new chapter on the analysis of direct representations.

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In the field of genetic and evolutionary algorithms (GEAs), a large amount of theory and empirical study has focused on operators and test problems, while problem representation has often been taken as given. This book breaks away from this tradition and provides a comprehensive overview on the influence of problem representations on GEA performance.
The book summarizes existing knowledge regarding problem representations and describes how basic properties of representations, such as redundancy, scaling, or locality, influence the performance of GEAs and other heuristic optimization methods. Using the developed theory, representations can be analyzed and designed in a theory-guided matter. The theoretical concepts are used for solving integer optimization problems and network design problems more efficiently.
The book is written in an easy-to-read style and is intended for researchers, practitioners, and students who want to learn about representations. This second edition extends the analysis of the basic properties of representations and introduces a new chapter on the analysis of direct representations.

Content:
Front Matter....Pages I-XVII
Introduction....Pages 1-7
Representations for Genetic and Evolutionary Algorithms....Pages 9-32
Three Elements of a Theory of Representations....Pages 33-96
Time-Quality Framework for a Theory-Based Analysis and Design of Representations....Pages 97-115
Analysis of Binary Representations of Integers....Pages 117-140
Analysis and Design of Representations for Trees....Pages 141-215
Analysis and Design of Search Operators for Trees....Pages 217-239
Performance of Genetic and Evolutionary Algorithms on Tree Problems....Pages 241-273
Summary and Conclusions....Pages 275-280
Back Matter....Pages 281-325

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In the field of genetic and evolutionary algorithms (GEAs), a large amount of theory and empirical study has focused on operators and test problems, while problem representation has often been taken as given. This book breaks away from this tradition and provides a comprehensive overview on the influence of problem representations on GEA performance.
The book summarizes existing knowledge regarding problem representations and describes how basic properties of representations, such as redundancy, scaling, or locality, influence the performance of GEAs and other heuristic optimization methods. Using the developed theory, representations can be analyzed and designed in a theory-guided matter. The theoretical concepts are used for solving integer optimization problems and network design problems more efficiently.
The book is written in an easy-to-read style and is intended for researchers, practitioners, and students who want to learn about representations. This second edition extends the analysis of the basic properties of representations and introduces a new chapter on the analysis of direct representations.

Content:
Front Matter....Pages I-XVII
Introduction....Pages 1-7
Representations for Genetic and Evolutionary Algorithms....Pages 9-32
Three Elements of a Theory of Representations....Pages 33-96
Time-Quality Framework for a Theory-Based Analysis and Design of Representations....Pages 97-115
Analysis of Binary Representations of Integers....Pages 117-140
Analysis and Design of Representations for Trees....Pages 141-215
Analysis and Design of Search Operators for Trees....Pages 217-239
Performance of Genetic and Evolutionary Algorithms on Tree Problems....Pages 241-273
Summary and Conclusions....Pages 275-280
Back Matter....Pages 281-325
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