Genetic algorithms are based on the principle of evolution, i.e., survival of the fittest. Hence evolution programming techniques, using genetic algorithms, are applicable to many hard optimization problems, such as optimization of functions with linear and nonlinear constraints, the travelling salesman problem, and problems of scheduling, partitioning, and control. For the expanding area of parallel computation these techniques are becoming more and more important. The book is self-contained and the only prerequisite is basic undergraduate mathematics. 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 in this task.

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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