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Genetic Algorithms + Data Structures = Evolution Programs

  • Book
  • © 1994

Overview

Authors:
  1. Zbigniew Michalewicz

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

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Table of contents (14 chapters)

  1. Front Matter

    Pages I-XVI
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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 55-91

  4. Numerical Optimization

    1. Front Matter

      Pages 93-93
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    2. Binary or Float?

      • Zbigniew Michalewicz
      Pages 95-104

    3. Fine Local Tuning

      • Zbigniew Michalewicz
      Pages 105-118

    4. Handling Constraints

      • Zbigniew Michalewicz
      Pages 119-166

    5. Evolution Strategies and Other Methods

      • Zbigniew Michalewicz
      Pages 167-184

  5. Evolution Programs

    1. Front Matter

      Pages 185-185
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    2. The Transportation Problem

      • Zbigniew Michalewicz
      Pages 187-209

    3. The Traveling Salesman Problem

      • Zbigniew Michalewicz
      Pages 211-237

    4. Drawing Graphs, Scheduling, Partitioning, and Path Planning

      • Zbigniew Michalewicz
      Pages 239-269

    5. Machine Learning

      • Zbigniew Michalewicz
      Pages 271-285

  6. Conclusions

    1. Conclusions

      • Zbigniew Michalewicz
      Pages 287-308

  7. Back Matter

    Pages 309-340
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Keywords

About this book

Genetic algorithms are founded upon the principle of evolution, i.e., survival of the fittest. Hence evolution programming techniques, based on genetic algorithms, are applicable to many hard optimization problems, such as optimization of functions with linear and nonlinear constraints, the traveling salesman problem, and problems of scheduling, partitioning, and control. The importance of these techniques has been growing in the last decade, since evolution programs are parallel in nature, and parallelism is one of the most promising directions in computer science.
The book is self-contained and the only prerequisite is basic undergraduate mathematics. It is aimed at researchers, practitioners, and graduate students in computer science and artificial intelligence, operations research, and engineering.
This second edition includes several new sections and many references to recent developments. A simple example of genetic code and an index are also added. Writing an evolution program for a given problem should be an enjoyable experience - this book may serve as a guide to this task.

Authors and Affiliations

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

    Zbigniew Michalewicz

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