Synopsis
I. Genetic Algorithms.- 1 GAs: What Are They?.- 2 GAs: How Do They Work?.- 3 GAs: Why Do They Work?.- 4 GAs: Selected Topics.- II. Numerical Optimization.- 5 Binary or Float?.- 6 Fine Local Tuning.- 7 Handling Constraints.- 8 Evolution Strategies and Other Methods.- III. Evolution Programs.- 9 The Transportation Problem.- 10 The Traveling Salesman Problem.- 11 Drawing Graphs, Scheduling, Partitioning, and Path Planning.- 12 Machine Learning.- Conclusions.- References.
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Review
Zbigniew Michalewicz's Genetic Algorithms + Data Structures = Evolution Programs has three sections. The first section is a straightforward introduction to genetic algorithms. In the second section, Michalewicz describes how to apply genetic algorithms to numerical optimization. Michalewicz, who is a pioneer in this field, discusses the rationale for using genetic algorithms for numerical optimization and describes several experiments that show how this new type of genetic algorithm performs. The author devotes the third section of the book to evolution programs.
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