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Search - An Introduction to Genetic Algorithms (Complex Adaptive Systems)

An Introduction to Genetic Algorithms - Complex Adaptive Systems
Author: Melanie Mitchell

"This is the best general book on Genetic Algorithms written to date. It covers background, history, and motivation; it selects important, informative examples of applications and discusses the use of Genetic Algorithms in scientific models; and it gives a good account of the status of the theory of Genetic Algorithms. Best of all the book prese... more »nts its material in clear, straightforward, felicitous prose, accessible to anyone with a college-level scientific background. If you want a broad, solid understanding of Genetic Algorithms -- where they came from, what's being done with them, and where they are going -- this is the book. -- John H. Holland, Professor, Computer Science and Engineering, and Professor of Psychology, The University of Michigan; External Professor, the Santa Fe Institute. Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics -- particularly in machine learning, scientific modeling, and artificial life -- and reviews a broad span of research, including the work of Mitchell and her colleagues. The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting "general purpose" nature of genetic algorithms as search methods that can be employed across disciplines. An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text. The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation.

ISBN-13: 9780262133166
ISBN-10: 0262133164
Publication Date: 2/27/1996
Pages: 224
Rating:

0 stars, based on 0 rating

Publisher: The MIT Press
Book Type: Hardcover
Other Versions: Paperback
Members Wishing: 0
Reviews: Amazon | Write a Review

Genres:

Computers & Technology >> Computer Science >> AI & Machine Learning >> Artificial Life
Computers & Technology >> Computer Science >> AI & Machine Learning >> General
Computers & Technology >> Computer Science >> AI & Machine Learning >> Intelligence & Semantics
Computers & Technology >> Computer Science >> AI & Machine Learning >> Computer Mathematics
Computers & Technology >> Programming >> Algorithms >> General
Computers & Technology >> Programming >> Algorithms >> Genetic
Computers & Technology >> General
Science & Math >> Evolution >> Genetics
Engineering & Transportation >> Professional Science >> Evolution >> Genetics

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