Bull L., Kovacs T. (eds.) Foundations of Learning Classifier Systems

Springer, 2005. — 336 p.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.Section I - 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 II - Credit Assignment
Reinforcement Learning: A Brief overview
A Mathematical Framework for Studying Learning in Classifier Systems
Rule Fitness and Pathology in Learning Classifier Systems
Learning Classifier Systems: A Reinforcement Learning Perspective.
Learning Classifier System with Convergence and Generalization
Section III - Problem Characterization
On the Classification of Maze Problems
What Makes a Problem Hard for XCS?