© 2006

Representations for Genetic and Evolutionary Algorithms


Table of contents

  1. Front Matter
    Pages I-XVII
  2. Franz Rothlauf
    Pages 1-7
  3. Franz Rothlauf
    Pages 117-140
  4. Franz Rothlauf
    Pages 275-280
  5. Back Matter
    Pages 281-325

About this book


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.


Evolution GEA Genetic and Evolutionary Algorithms Integer Representations Operator Optimal Communication Spanning Tree Problems Optimization Methods Theory of Representation Tree Representations algorithm algorithms calculus evolutionary algorithm knowledge optimization

Authors and affiliations

  1. 1.School of Business AdministrationUniversität MannheimMannheimGermany

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