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Rule-Based Evolutionary Online Learning Systems

A Principled Approach to LCS Analysis and Design

  • Book
  • © 2006

Overview

Authors:
  1. Martin V. Butz

    1. Department of Cognitive Psychology, University of Würzburg, Würzburg, Germany

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  • Provides a comprehensive introduction to Learning Classifiers Systems
  • Principle approach to understand, analyze, and design Learning Classifier Systems
  • Includes supplementary material: sn.pub/extras

Part of the book series: Studies in Fuzziness and Soft Computing (STUDFUZZ, volume 191)

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Table of contents (13 chapters)

  1. Front Matter

    Pages I-XXI
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  2. Introduction

    • Martin V. Butz
    Pages 1-7

  3. Prerequisites

    • Martin V. Butz
    Pages 9-30

  4. Simple Learning Classifier Systems

    • Martin V. Butz
    Pages 31-50

  5. The XCS Classifier System

    • Martin V. Butz
    Pages 51-64

  6. How XCS Works: Ensuring Effective Evolutionary Pressures

    • Martin V. Butz
    Pages 65-90

  7. When XCS Works: Towards Computational Complexity

    • Martin V. Butz
    Pages 91-122

  8. Effective XCS Search: Building Block Processing

    • Martin V. Butz
    Pages 123-146

  9. XCS in Binary Classification Problems

    • Martin V. Butz
    Pages 147-156

  10. XCS in Multi-Valued Problems

    • Martin V. Butz
    Pages 157-179

  11. XCS in Reinforcement Learning Problems

    • Martin V. Butz
    Pages 181-195

  12. Facetwise LCS Design

    • Martin V. Butz
    Pages 197-206

  13. Towards Cognitive Learning Classifier Systems

    • Martin V. Butz
    Pages 207-217

  14. Summary and Conclusions

    • Martin V. Butz
    Pages 219-225

  15. Back Matter

    Pages 227-266
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Keywords

About this book

Rule-basedevolutionaryonlinelearningsystems,oftenreferredtoasMichig- style learning classi?er systems (LCSs), were proposed nearly thirty years ago (Holland, 1976; Holland, 1977) originally calling them cognitive systems. LCSs combine the strength of reinforcement learning with the generali- tion capabilities of genetic algorithms promising a ?exible, online general- ing, solely reinforcement dependent learning system. However, despite several initial successful applications of LCSs and their interesting relations with a- mal learning and cognition, understanding of the systems remained somewhat obscured. Questions concerning learning complexity or convergence remained unanswered. Performance in di?erent problem types, problem structures, c- ceptspaces,andhypothesisspacesstayednearlyunpredictable. Thisbookhas the following three major objectives: (1) to establish a facetwise theory - proachforLCSsthatpromotessystemanalysis,understanding,anddesign;(2) to analyze, evaluate, and enhance the XCS classi?er system (Wilson, 1995) by the means of the facetwise approach establishing a fundamental XCS learning theory; (3) to identify both the major advantages of an LCS-based learning approach as well as the most promising potential application areas. Achieving these three objectives leads to a rigorous understanding of LCS functioning that enables the successful application of LCSs to diverse problem types and problem domains. The quantitative analysis of XCS shows that the inter- tive, evolutionary-based online learning mechanism works machine learning competitively yielding a low-order polynomial learning complexity. Moreover, the facetwise analysis approach facilitates the successful design of more - vanced LCSs including Holland’s originally envisioned cognitivesystems. Martin V.

Authors and Affiliations

  • Department of Cognitive Psychology, University of Würzburg, Würzburg, Germany

    Martin V. Butz

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