Genetic Algorithms + Data Structures = Evolution Programs

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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'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

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

  Zbigniew Michalewicz

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