Introduction to Genetic Algorithms

ISBN-10: 0262631857
ISBN-13: 9780262631853
Edition: 1998 (Reprint)
Authors: Melanie Mitchell
List price: $45.00 Buy it from $8.85
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Description: Genetic algorithms have been used in science and engineering as adaptive algorithms for solving problems and as computational models of natural evolutionary systems. This brief introduction describes some of the most interesting research in the  More...

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Book details

List price: $45.00
Copyright year: 1998
Publisher: MIT Press
Publication date: 3/2/1998
Binding: Paperback
Pages: 221
Size: 7.00" wide x 10.00" long x 0.50" tall
Weight: 0.528

Genetic algorithms have been used in science and engineering as adaptive algorithms for solving problems and as computational models of natural evolutionary systems. This brief introduction describes some of the most interesting research in the field.

Elias Tuma is Professor of Economics at the University of California, Davis.

Preface
Acknowledgments
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?
Genetic Algorithms in Problem Solving
Evolving Computer Programs
Data Analysis and Prediction
Evolving Neural Networks
Genetic Algorithms in Scientific Models
Modeling Interactions Between Learning And Evolution
Modeling Sexual Selection
Modeling Ecosystems
Measuring Evolutionary Activity
Theoretical Foundations of Genetic Algorithms
Schemas and the Two-Armed Bandit Problem
Royal Roads
Exact Mathematical Models Of Simple Genetic Algorithms
Statistical-Mechanics Approaches
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
Conclusions and Future Directions
Incorporating Ecological Interactions
Incorporating New Ideas from Genetics
Incorporating Development and Learning
Adapting Encodings and Using Encodings That Permit Hierarchy and Open-Endedness
Adapting Parameters
Connections with the Mathematical Genetics Literature
Extension of Statistical Mechanics Approaches
Identifying and Overcoming Impediments to the Success of GAs
Understanding the Role of Schemas in GAs
Understanding the Role of Crossover
Theory of GAs With Endogenous Fitness
Selected General References
Other Resources
Selected Journals Publishing Work on Genetic Algorithms
Selected Annual or Biannual Conferences Including Work on Genetic Algorithms
Internet Mailing Lists, World Wide Web Sites, and News Groups with Information and Discussions on Ge...
Bibliography
Index

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