Foundations of Learning Classifier Systems

Foundations of Learning Classifier Systems edited by Larry Bull, Tim Kovacs.

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 computati...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Bull, Larry (Editor), Kovacs, Tim (Editor)
Format: eBook
Language:English
Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2005.
Edition:1st ed. 2005.
Series:Studies in Fuzziness and Soft Computing, 183
Springer eBook Collection.
Subjects:
Applied mathematics.
Engineering mathematics.
Artificial intelligence.
Bioinformatics.
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
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?

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