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Introduction to Evolutionary Computing

  • A.E. Eiben
  • J.E. Smith

Part of the Natural Computing Series book series (NCS)

Table of contents

  1. Front Matter
    Pages I-XII
  2. The Basics

    1. Front Matter
      Pages 1-1
    2. A. E. Eiben, J. E. Smith
      Pages 1-12
    3. A. E. Eiben, J. E. Smith
      Pages 13-24
    4. A. E. Eiben, J. E. Smith
      Pages 25-48
    5. A. E. Eiben, J. E. Smith
      Pages 49-78
    6. A. E. Eiben, J. E. Smith
      Pages 79-98
    7. A. E. Eiben, J. E. Smith
      Pages 99-116
  3. Methodological Issues

    1. Front Matter
      Pages 117-117
    2. A. E. Eiben, J. E. Smith
      Pages 119-129
    3. A. E. Eiben, J. E. Smith
      Pages 131-146
    4. A. E. Eiben, J. E. Smith
      Pages 147-163
  4. Advanced Topics

    1. Front Matter
      Pages 165-165
    2. A. E. Eiben, J. E. Smith
      Pages 167-183
    3. A. E. Eiben, J. E. Smith
      Pages 185-194
    4. A. E. Eiben, J. E. Smith
      Pages 195-202
    5. A. E. Eiben, J. E. Smith
      Pages 203-213
    6. A. E. Eiben, J. E. Smith
      Pages 215-222
    7. A. E. Eiben, J. E. Smith
      Pages 223-229
    8. A. E. Eiben, J. E. Smith
      Pages 231-244
    9. A. E. Eiben, J. E. Smith
      Pages 245-258
  5. Back Matter
    Pages 259-287

About this book

Introduction

The overall structure of this new edition is three-tier: Part I presents the basics, Part II is concerned with methodological issues, and Part III discusses advanced topics. In the second edition the authors have reorganized the material to focus on problems, how to represent them, and then how to choose and design algorithms for different representations. They also added a chapter on problems, reflecting the overall book focus on problem-solvers, a chapter on parameter tuning, which they combined with the parameter control and "how-to" chapters into a methodological part, and finally a chapter on evolutionary robotics with an outlook on possible exciting developments in this field.

The book is suitable for undergraduate and graduate courses in artificial intelligence and computational intelligence, and for self-study by practitioners and researchers engaged with all aspects of bioinspired design and optimization.

Keywords

Estimation of Distribution Algorithms (EDA) Evolution Strategies (ES) Evolutionary Algorithm (EA) Evolutionary Computing (EC) Evolutionary Programming (EP) Evolutionary Robotics Genetic Algorithms (GA) Genetic Programming (GP) Learning Classifier Systems (LCS) Memetic Algorithms Optimization

Authors and affiliations

  • A.E. Eiben
    • 1
  • J.E. Smith
    • 2
  1. 1.Dept. of Computer ScienceVU University AmsterdamAmsterdamThe Netherlands
  2. 2.Department of Computer Science and Creative TechnologiesThe University of the West of EnglandBristolUnited Kingdom

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-662-44874-8
  • Copyright Information Springer-Verlag Berlin Heidelberg 2015
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-662-44873-1
  • Online ISBN 978-3-662-44874-8
  • Series Print ISSN 1619-7127
  • Buy this book on publisher's site