Representations for Genetic and Evolutionary Algorithms (Studies in Fuzziness and Soft Computing, 104)

Franz Rothlauf

ISBN 10: 3790814962 / ISBN 13: 9783790814965

Published by Physica, 2002

About this Item

Ancien livre de bibliothèque avec équipements. Edition 2002. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Very good. Former library book. Edition 2002. Ammareal gives back up to 15% of this item's net price to charity organizations.

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Bibliographic Details

• Title: Representations for Genetic and Evolutionary Algorithms (Studies in Fuzziness and Soft Computing, 104)
• Publisher: Physica
• Publication Date: 2002
• Language: English
• ISBN 10: 3790814962
• ISBN 13: 9783790814965
• Binding: Hardcover
• Condition: Très bon

About this title

Synopsis

In the field of genetic and evolutionary algorithms (GEAs), much theory and empirical study has been heaped upon operators and test problems, but problem representation has often been taken as given. This monograph studies a number of critical elements of a theory of representations for GEAs and applies them to the empirical study of various important idealized test functions and problems of commercial import. The book considers basic concepts of representations, such as redundancy, scaling and locality and describes how GEAs' performance is influenced. Using the developed theory representations can be analyzed and designed in a theory-guided manner. The theoretical concepts are used as examples for efficiently solving integer optimization problems and network design problems. The results show that proper representations are crucial for GEAs' success.

From the Back Cover

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.