Genetic Algorithms + Data Structures = Evolution Programs

  • Zbigniew Michalewicz

Part of the Artificial Intelligence book series (AI)

Table of contents

  1. Front Matter
    Pages I-XIV
  2. Introduction

    1. Zbigniew Michalewicz
      Pages 1-10
  3. Genetic Algorithms

    1. Front Matter
      Pages 11-11
    2. Zbigniew Michalewicz
      Pages 13-30
    3. Zbigniew Michalewicz
      Pages 31-42
    4. Zbigniew Michalewicz
      Pages 43-53
    5. Zbigniew Michalewicz
      Pages 55-72
  4. Numerical Optimization

    1. Front Matter
      Pages 73-73
    2. Zbigniew Michalewicz
      Pages 75-82
    3. Zbigniew Michalewicz
      Pages 83-96
    4. Zbigniew Michalewicz
      Pages 97-126
    5. Zbigniew Michalewicz
      Pages 127-138
  5. Evolution Programs

    1. Front Matter
      Pages 139-139
    2. Zbigniew Michalewicz
      Pages 141-163
    3. Zbigniew Michalewicz
      Pages 165-191
    4. Zbigniew Michalewicz
      Pages 193-214
    5. Zbigniew Michalewicz
      Pages 215-229
    6. Zbigniew Michalewicz
      Pages 231-239
  6. Back Matter
    Pages 241-252

About this book

Introduction

'What does your Master teach?' asked a visitor. 'Nothing,' said the disciple. 'Then why does he give discourses?' 'He only points the way - he teaches nothing.' Anthony de Mello, One Minute Wisdom During the last three decades there has been a growing interest in algorithms which rely on analogies to natural processes. The emergence of massively par­ allel computers made these algorithms of practical interest. The best known algorithms in this class include evolutionary programming, genetic algorithms, evolution strategies, simulated annealing, classifier systems, and neural net­ works. Recently (1-3 October 1990) the University of Dortmund, Germany, hosted the First Workshop on Parallel Problem Solving from Nature [164]. This book discusses a subclass of these algorithms - those which are based on the principle of evolution (survival of the fittest). In such algorithms a popu­ lation of individuals (potential solutions) undergoes a sequence of unary (muta­ tion type) and higher order (crossover type) transformations. These individuals strive for survival: a selection scheme, biased towards fitter individuals, selects the next generation. After some number of generations, the program converges - the best individual hopefully represents the optimum solution. There are many different algorithms in this category. To underline the sim­ ilarities between them we use the common term "evolution programs" .

Keywords

algorithm algorithms artificial intelligence computer science control data structure data structures evolution genetic algorithms intelligence mathematics operations research optimization programming scheduling

Authors and affiliations

  • Zbigniew Michalewicz
    • 1
  1. 1.Department of Computer ScienceUniversity of North CarolinaCharlotteUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-662-02830-8
  • Copyright Information Springer-Verlag Berlin Heidelberg 1992
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-662-02832-2
  • Online ISBN 978-3-662-02830-8
  • Series Print ISSN 1431-0066
  • About this book