
Foundations of Learning Classifier Systems
Springer (Publisher)
Published on 25. November 2010
Book
Paperback/Softback
VI, 336 pages
978-3-642-06413-5 (ISBN)
Description
This volume brings together recent theoretical work in Learning Classifier Systems (LCS), which is a Machine Learning technique combining Genetic Algorithms and Reinforcement Learning. It includes self-contained background chapters on related fields (reinforcement learning and evolutionary computation) tailored for a classifier systems audience and written by acknowledged authorities in their area - as well as a relevant historical original work by John Holland.
More details
Series
Edition
Softcover reprint of hardcover 1st ed. 2005
Language
English
Place of publication
Berlin
Germany
Publishing group
Springer Berlin
Target group
Professional and scholarly
Research
Illustrations
VI, 336 p.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 19 mm
Weight
522 gr
ISBN-13
978-3-642-06413-5 (9783642064135)
DOI
10.1007/b100387
Schweitzer Classification
Other editions
Additional editions

Larry Bull | Tim Kovacs
Foundations of Learning Classifier Systems
Book
07/2005
Springer
€160.49
Shipment within 7-9 days
Content
Section 1 - Rule Discovery. Population Dynamics of Genetic Algorithms. Approximating Value Functions in Classifier Systems. Two Simple Learning Classifier Systems. Computational Complexity of the XCS Classifier System. An Analysis of Continuous-Valued Representations for Learning Classifier Systems.- Section 2 - Credit Assignment. Reinforcement Learning: a Brief Overview. A Mathematical Framework for Studying Learning Classifier Systems. Rule Fitness and Pathology in Learning Classifier Systems. Learning Classifier Systems: A Reinforcement Learning Perspective. Learning Classifier Systems with Convergence and Generalization.- Section 3 - Problem Characterization. On the Classification of Maze Problems. What Makes a Problem Hard?