Foundations of Learning Classifier Systems

by Larry Bull (Editor), Tim Kovacs (Editor)

Overview

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.

Product Details

ISBN-13:	9783540250739
Publisher:	Springer Berlin Heidelberg
Publication date:	09/26/2005
Series:	Studies in Fuzziness and Soft Computing , #183
Edition description:	2005
Pages:	336
Product dimensions:	6.10(w) x 9.25(h) x 0.03(d)

Table of Contents

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?