Overview
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Table of contents (14 chapters)
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Introduction
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Genetic Algorithms
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Numerical Optimization
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Evolution Programs
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Conclusions
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
Bibliographic Information
Book Title: Genetic Algorithms + Data Structures = Evolution Programs
Authors: Zbigniew Michalewicz
DOI: https://doi.org/10.1007/978-3-662-07418-3
Publisher: Springer Berlin, Heidelberg
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eBook Packages: Springer Book Archive
Copyright Information: Springer-Verlag Berlin Heidelberg 1994
eBook ISBN: 978-3-662-07418-3Published: 09 March 2013
Edition Number: 2
Number of Pages: XVI, 340
Number of Illustrations: 26 b/w illustrations
Additional Information: Originally published in the series: Artificial Intelligence
Topics: Artificial Intelligence, Algorithms, Numerical Analysis, Programming Techniques, Software Engineering, Operations Research/Decision Theory
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