Genetic algorithms in search, optimization, and machine learning / by David E. Goldberg.

By: Goldberg, David E. (David Edward), 1953-.

Material type: materialTypeLabel

BookPublisher: Reading, Mass. : Addison-Wesley Pub. Co, 1989Description: xiii, 412 p. : ill. ; 25 cm.ISBN: 0201157675.Subject(s): Genetic algorithms

| Machine learning

DDC classification: 006.31

Contents:

A gentle introduction to genetic algorithms -- Genetic algorithms revisited: mathematical foundations -- Computer implementation of a genetic algorithm -- Some applications of genetic algorithms -- Advanced operators and techniques in genetic search -- Introduction to genetic-based machine learning -- Applications of genetics-based machine learning -- A look back, a glance ahead.

Holdings
Item type	Current library	Call number	Copy number	Status	Date due	Barcode	Item holds
General Lending	MTU Bishopstown Library Store Item	006.31 (Browse shelf(Opens below))	1	Available		00017554
General Lending	MTU Bishopstown Library Lending	006.31 (Browse shelf(Opens below))	1	Available		00086044

Total holds: 0

Enhanced descriptions from Syndetics:

This book describes the theory, operation, and application of genetic algorithms-search algorithms based on the mechanics of natural selection and genetics.

Bibliography: (pages 381-401) and index.

Table of contents provided by Syndetics

Genetic Algorithms Revisited: Mathematical Foundations
Computer Implementation of a Genetic Algorithm
Some Applications of Genetic Algorithms
Advanced Operators and Techniques in Genetic Search
Introduction to Genetics-Based Machine Learning
Applications of Genetics-Based Machine Learning
A Look Back, A Glance Ahead
Appendixes

Reviews provided by Syndetics

CHOICE Review

If one takes seriously Darwin's theory of evolution by natural selection, then it is reasonable to base optimization methods on selection. Genetic algorithms are a caricature of real genetic systems--chromosomes are replaced by bit vectors representing possible solutions to a problem. A measure of fitness is assigned to a vector and the probability of a vector's participating in the production of the next generation of vectors is proportional to this fitness measure. Goldberg presents genetic algorithms as they have been developed by John Holland's group at the University of Michigan; this is the first accessible, systematic introduction to their work. Goldberg clearly describes the methods for designing and running genetic algorithms, giving Pascal programs for a number of simple algorithms. He reports on better software environments that have been developed to allow easy creation of genetic algorithms, but the small Pascal programs should allow the interested reader to build and test some simple genetic algorithms. The book's major shortcoming is that it concentrates on the work of one group and ignores the work of others, in particular, the whole line of work using genetic algorithms in numerical function optimization. The difficulty of problem representation is also shortchanged. Why are genetic algorithms important? One major reason is that these algorithms parallelize automatically. For this reason genetic and neural-net algorithms may be the wave of the future. Because of this possibility and because Goldberg clearly presents genetic algorithms, this book is a must for every academic library. -P. Cull, Oregon State University