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Genetic Algorithms + Data Structures = Evolution Programs

  • Book
  • © 1992

Overview

Authors:
  1. Zbigniew Michalewicz

    1. Department of Computer Science, University of North Carolina, Charlotte, USA

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Part of the book series: Artificial Intelligence (AI)

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Table of contents (14 chapters)

  1. Front Matter

    Pages I-XIV
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  2. Introduction

    1. Introduction

      • Zbigniew Michalewicz
      Pages 1-10

  3. Genetic Algorithms

    1. Front Matter

      Pages 11-11
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    2. GAs: What Are They?

      • Zbigniew Michalewicz
      Pages 13-30

    3. GAs: How Do They Work?

      • Zbigniew Michalewicz
      Pages 31-42

    4. GAs: Why Do They Work?

      • Zbigniew Michalewicz
      Pages 43-53

    5. GAs: Selected Topics

      • Zbigniew Michalewicz
      Pages 55-72

  4. Numerical Optimization

    1. Front Matter

      Pages 73-73
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    2. Binary or Float?

      • Zbigniew Michalewicz
      Pages 75-82

    3. Fine Local Tuning

      • Zbigniew Michalewicz
      Pages 83-96

    4. Handling Constraints

      • Zbigniew Michalewicz
      Pages 97-126

    5. Evolution Strategies and Other Methods

      • Zbigniew Michalewicz
      Pages 127-138

  5. Evolution Programs

    1. Front Matter

      Pages 139-139
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    2. The Transportation Problem

      • Zbigniew Michalewicz
      Pages 141-163

    3. The Traveling Salesman Problem

      • Zbigniew Michalewicz
      Pages 165-191

    4. Drawing Graphs, Scheduling, and Partitioning

      • Zbigniew Michalewicz
      Pages 193-214

    5. Machine Learning

      • Zbigniew Michalewicz
      Pages 215-229

    6. Conclusions

      • Zbigniew Michalewicz
      Pages 231-239

  6. Back Matter

    Pages 241-252
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Keywords

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" .

Authors and Affiliations

  • Department of Computer Science, University of North Carolina, Charlotte, USA

    Zbigniew Michalewicz

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