book announcement
Melanie Mitchell
An Introduction to Genetic Algorithms
by Melanie Mitchell
MIT Press
Complex Adaptive Systems series.
A Bradford Book
Available January 1996
ISBN 0-262-13316-4
232 pp.
$30.00
From the book jacket:
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.
Melanie Mitchell is Research Professor and Director of the Adaptive
Computation Program at the Santa Fe Institute.
Complex Adaptive Systems series. A Bradford Book
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Table of contents:
Chapter 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
Chapter 2: Genetic Algorithms in Problem-Solving
Evolving Computer Programs
Data Analysis and Prediction
Evolving Neural Networks
Thought Exercises
Computer Exercises
Chapter 3: Genetic Algorithms in Scientific Models
Modeling Interactions Between Learning and Evolution
Modeling Sexual Selection
Modeling Ecosystems
Measuring Evolutionary Activity
Thought Exercises
Computer Exercises
Chapter 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
Chapter 5: Implementing a Genetic Algorithm
Introduction
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
Chapter 6: Conclusions and Future Directions
Appendices
Selected General References
Other Resources
Bibliography
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