Cover image for Foundations of Learning Classifier Systems
Foundations of Learning Classifier Systems
Title:
Foundations of Learning Classifier Systems
Author:
Bull, Larry. editor.
ISBN:
9783540323969
Physical Description:
VI, 336 p. online resource.
Series:
Studies in Fuzziness and Soft Computing, 183
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?
Abstract:
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.
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