An Introduction to Genetic Algorithms / Edition 1

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Overview

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 GeneticAlgorithms 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.

Mitchell's book provvides an in-depth intodution to genetic algorithms in areas such as machine learning , scientific modeling, and "artificial life". _An Introduction to Genetic Algorithms_ is a terse and accesible text allowing readers to implement and experiment with genetic algorithms (GA's) -- specifically GA's in machine learning, scientific modeling, and "artificial life". Included are thought experiments and exercises to enforce ideas presented in each chapter of the text. Chapter one introduces GA terms and history while describing two applications in detail. Chapter two and three explore the use of GA's in problem solving and scientific modeling. The fourth chapter gives a thorough overview of the theoretical foundations of GA's, while the fifth tackles implementation of GA's. The last chapter surveys some currently unanswered questions and considers the future of GA's. Inlcuded in two appendices are substantial references to other resources on genetic algorithms.

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What People Are Saying

From the Publisher
"An outstanding introduction to a new and important field of computer science."Tim Watson , The Computer Journal

"This is a useful introduction to the subject and is well worth reading as an entry into evolutionary computing." Chris Robbins ,Computing

"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 presents 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 ofMichigan;External Professor, the Santa Fe Institute.

Tim Watson, The Computer Journal
An outstanding introduction to a new and important field of computer science.
Chris Robbins, Computing
This is a useful introduction to the subject and is well worth reading as an entry into evolutionary computing.
John H. Holland
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 presents 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.

Read More Show Less

Product Details

  • ISBN-13: 9780262631853
  • Publisher: MIT Press
  • Publication date: 2/6/1998
  • Series: Complex Adaptive Systems , #13
  • Edition description: Reprint
  • Edition number: 1
  • Pages: 221
  • Sales rank: 551,633
  • Product dimensions: 6.80 (w) x 9.90 (h) x 0.50 (d)

Table of Contents

Preface
Acknowledgments
1 Genetic Algorithms: An Overview 1
2 Genetic Algorithms in Problem Solving 35
3 Genetic Algorithms in Scientific Models 85
4 Theoretical Foundations of Genetic Algorithms 117
5 Implementing a Genetic Algorithm 155
6 Conclusions and Future Directions 181
Appendix A Selected General References 187
Appendix B Other Resources 189
Bibliography 191
Index 203
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  • Anonymous

    Posted August 5, 2005

    Great Overview

    This book offers a great overview of Genetic Algorithms and some applications. It does not offer exhaustive coverage of the topics, but it clearly explains the basic principles. It makes for an interesting read, and gives you enough to get started in the subject.

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