An Introduction to Genetic Algorithms / Edition 1

by Melanie Mitchell

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 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.

Product Details

ISBN-13:	9780262631853
Publisher:	MIT Press
Publication date:	03/02/1998
Series:	Complex Adaptive Systems , #13
Edition description:	Reprint
Pages:	221
Product dimensions:	7.06(w) x 10.00(h) x 0.48(d)
Age Range:	18 Years

About the Author

Melanie Mitchell, Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Michigan, is a Fellow of the Michigan Society of Fellows. She is also Director of the Adaptive Computation Program at the Santa Fe Institute.

Table of Contents

1. Genetic Algorithms: An Overview
A Brief History of Evolutionary Computation – The Appeal of
Evolution – Biological Terminology – Search Spaces and Fitness
Landscapes – Elements of Genetic Algorithms – A Simple Genetic
Algorithm – Genetic Algorithms and Traditional Search Methods –
Some Applications of Genetic Algorithms – Two Brief Examples –
How Do Genetic Algorithms Work? – Thought Exercises – Computer
Exercises
2 . Genetic Algorithms in Problem Solving
Evolving Computer Programs – Data Analysis and Prediction
– Evolving Neural Networks – Thought Exercises – Computer
Exercises
3. Genetic Algorithms in Scientific Models
Modeling Interactions Between Learning and Evolution – Modeling
Sexual Selection – Modeling Ecosystems – Measuring Evolutionary
Activity – Thought Exercises – Computer Exercises
4. Theoretical Foundations of Genetic Algorithms
Schemas and the Two-Armed Bandit Problem – Royal Roads –
Exact Mathematical Models of Simple Genetic Algorithms – Statistical-Mechanics
Approaches – Thought Exercises – Computer Exercises
5. Implementing a Genetic Algorithm
When Should a Genetic Algorithm Be Used? – Encoding a Problem
for a Genetic Algorithm – Adapting the Encoding – Selection Methods
– Genetic Operators – Parameters for Genetic Algorithms –
Thought Exercises – Computer Exercises
6. Conclusions and Future Directions

What People are Saying About This

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.

Richard K. Belew

Genetic algorithms (GAs) are of increasing interest, both as computational models of natural systems and as algorithmic techniques for problem-solving. This text fills an important role for student interested in Gas for either reason. It coverage of recent, theoretical GA work also helps to build a common foundation for the biologists and computer scientists intrigued by what Gas have to offer.

John R. Koza

Melanie Mitchel has successfully assembled a collection of recent applications that convey the excitement and potential of genetic algorithms in solving an array of otherwise difficult or intractable problems.

David L. Waltz

Melanie Mitchell has written an excellent introduction to genetic algorithms, one of the most promising branches of machine learning. GAs, with their minimal demands on the programmer together with heavy exploitation of computing cycles, and uniquely well-positioned to take advantage of the vastly increased availability of computer cycles made covering both traditional GA methods and the recent wealth of GA variants, and also providing details on GA implementation, theoretical foundations, and scientific applications.

Endorsement

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 of Michigan; External Professor, the Santa Fe Institute

From the Publisher

Genetic algorithms (GAs) are of increasing interest, both as computational models of natural systems and as algorithmic techniques for problem-solving. This text fills an important role for student interested in Gas for either reason. It coverage of recent, theoretical GA work also helps to build a common foundation for the biologists and computer scientists intrigued by what Gas have to offer.

—Richard K. Belew, Associate Professor, Computer Science & Engineering Department, University of California, San Diego

Melanie Mitchell has written an outstanding—and needed—new text for the burgeoning field for genetic algorithms. The book combines a clear explanation of GA basics and implementation, penetrating discussions of 15 of the most significant recent GA researches in problem solving and scientific modelling, and the first major review of GA theory from Holland's original concepts to recent advances and controversies. There are over 200 references. Mitchell's work is sure to become the field's new standard source and text.

—Stewart W. Wilson, The Rowland Institute for Science

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 of Michigan; External Professor, the Santa Fe Institute

Melanie Mitchell has written an excellent introduction to genetic algorithms, one of the most promising branches of machine learning. GAs, with their minimal demands on the programmer together with heavy exploitation of computing cycles, and uniquely well-positioned to take advantage of the vastly increased availability of computer cycles made covering both traditional GA methods and the recent wealth of GA variants, and also providing details on GA implementation, theoretical foundations, and scientific applications.

—David L. Waltz, NEC Research Institute and Brandeis University

Melanie Mitchel has successfully assembled a collection of recent applications that convey the excitement and potential of genetic algorithms in solving an array of otherwise difficult or intractable problems.

—John R. Koza, Consulting Professor, Computer Science Department, Stanford University

Stewart W. Wilson

Melanie Mitchell has written an outstanding—and needed—new text for the burgeoning field for genetic algorithms. The book combines a clear explanation of GA basics and implementation, penetrating discussions of 15 of the most significant recent GA researches in problem solving and scientific modelling, and the first major review of GA theory from Holland's original concepts to recent advances and controversies. There are over 200 references. Mitchell's work is sure to become the field's new standard source and text.

Computing - Chris Robbins

This is a useful introduction to the subject and is well worth reading as an entry into evolutionary computing.

The Computer Journal - Tim Watson

An outstanding introduction to a new and important field of computer science.

Editorial Reviews

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—

From the Publisher

This is a useful introduction to the subject and is well worth reading as an entry into evolutionary computing.

—Chris Robbins, Computing

Reviews

This is a useful introduction to the subject and is well worth reading as an entry into evolutionary computing.

Computing - Chris Robbins

An outstanding introduction to a new and important field of computer science.

The Computer Journal - Tim Watson