Representations for Genetic and Evolutionary Algorithms

Rothlauf, Franz

ISBN 10: 354025059X / ISBN 13: 9783540250593

Editore: Springer, 2006

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Codice articolo ERICA758354025059X5

Dati bibliografici

• Titolo: Representations for Genetic and Evolutionary Algorithms
• Editore: Springer
• Data di pubblicazione: 2006
• Lingua: Inglese
• ISBN 10: 354025059X
• ISBN 13: 9783540250593
• Rilegatura: Hardcover
• Edizione: seconda edizione
• Condizione: Like New
• Tipo di libro: book

Descrizione articolo

Riassunto

In the field of genetic and evolutionary algorithms (GEAs), a large amount of theory and empirical study has been focused on operators and test problems, while problem representation has often been taken as given. This book breaks with 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-readable 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.

Dalla quarta di copertina

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.