© 1992

Genetic Algorithms + Data Structures = Evolution Programs


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


'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" .


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

Authors and affiliations

  1. 1.Department of Computer ScienceUniversity of North CarolinaCharlotteUSA

Bibliographic information