Overview
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (6 chapters)
-
Front Matter
-
Back Matter
Keywords
About this book
Genetic algorithms are general-purpose search algorithms that use principles inspired by natural population genetics to evolve solutions to problems. The basic idea is to maintain a population of knowledge structure that represent candidate solutions to the problem of interest. The population evolves over time through a process of competition (i.e. survival of the fittest) and controlled variation (i.e. recombination and mutation).
Genetic Algorithms for Machine Learning contains articles on three topics that have not been the focus of many previous articles on GAs, namely concept learning from examples, reinforcement learning for control, and theoretical analysis of GAs. It is hoped that this sample will serve to broaden the acquaintance of the general machine learning community with the major areas of work on GAs. The articles in this book address a number of central issues in applying GAs to machine learning problems. For example, the choice of appropriate representation and the corresponding set of genetic learning operators is an important set of decisions facing a user of a genetic algorithm.
The study of genetic algorithms is proceeding at a robust pace. If experimental progress and theoretical understanding continue to evolve as expected, genetic algorithms will continue to provide a distinctive approach to machine learning.
Genetic Algorithms for Machine Learning is an edited volume of original research made up of invited contributions by leading researchers.
Reviews
Editors and Affiliations
Bibliographic Information
Book Title: Genetic Algorithms for Machine Learning
Editors: John J. Grefenstette
DOI: https://doi.org/10.1007/978-1-4615-2740-4
Publisher: Springer New York, NY
-
eBook Packages: Springer Book Archive
Copyright Information: Springer Science+Business Media New York 1994
Hardcover ISBN: 978-0-7923-9407-5Published: 30 November 1993
Softcover ISBN: 978-1-4613-6182-4Published: 22 December 2012
eBook ISBN: 978-1-4615-2740-4Published: 06 December 2012
Edition Number: 1
Number of Pages: IV, 165
Topics: Artificial Intelligence