Table of contents

  1. Front Matter
    Pages i-x
  2. Pages 1-18
  3. Pages 19-63
  4. Pages 65-93
  5. Pages 95-109
  6. Pages 111-140
  7. Pages 231-263
  8. Pages 265-285
  9. Back Matter
    Pages 287-332

About this book

Introduction

Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory is a survey of some important theoretical contributions, many of which have been proposed and developed in the Foundations of Genetic Algorithms series of workshops. However, this theoretical work is still rather fragmented, and the authors believe that it is the right time to provide the field with a systematic presentation of the current state of theory in the form of a set of theoretical perspectives. The authors do this in the interest of providing students and researchers with a balanced foundational survey of some recent research on GAs. The scope of the book includes chapter-length discussions of Basic Principles, Schema Theory, "No Free Lunch", GAs and Markov Processes, Dynamical Systems Model, Statistical Mechanics Approximations, Predicting GA Performance, Landscapes and Test Problems.

Keywords

Stochastic Processes algorithms dynamical systems genetic algorithms linear algebra optimization

Authors and affiliations

  • Colin R. Reeves
    • 1
  • Jonathan E. Rowe
    • 2
  1. 1.School of Mathematical and Information SciencesCoventry UniversityUK
  2. 2.School of Computer ScienceUniversity of BirminghamUSA

Bibliographic information

  • DOI https://doi.org/10.1007/b101880
  • Copyright Information Kluwer Academic Publishers 2002
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4020-7240-6
  • Online ISBN 978-0-306-48050-8
  • Series Print ISSN 1387-666X
  • About this book