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Applications of Learning Classifier Systems

  • Book
  • © 2004

Overview

Editors:
  1. Larry Bull

    1. Faculty of Computing, Engineering & Mathematical Sciences, University of the West of England, Bristol, UK

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  • Brings together recent real-world applications of a machine learning technique whose performance has been greatly improved in recent years and which is experiencing resurgence in interest
  • Includes supplementary material: sn.pub/extras

Part of the book series: Studies in Fuzziness and Soft Computing (STUDFUZZ, volume 150)

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Table of contents (12 chapters)

  1. Front Matter

    Pages I-VIII
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  2. Learning Classifier Systems: A Brief Introduction

    1. Learning Classifier Systems: A Brief Introduction

      • Larry Bull
      Pages 1-12

  3. Data Mining

    1. Front Matter

      Pages 13-13
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    2. Data Mining using Learning Classifier Systems

      • Alwyn Barry, John Holmes, Xavier Llorà
      Pages 15-67

    3. NXCS Experts for Financial Time Series Forecasting

      • Giuliano Armano
      Pages 68-91

    4. Encouraging Compact Rulesets from XCS for Enhanced Data Mining

      • Philip W. Dixon, David W. Corne, Martin J. Oates
      Pages 92-109

  4. Modelling and Optimization

    1. Front Matter

      Pages 111-111
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    2. The Fighter Aircraft LCS: A Real-World, Machine Innovation Application

      • R. E. Smith, A. El-Fallah, B. Ravichandran, R. K. Mehra, B. A. Dike
      Pages 113-142

    3. Traffic Balance using Classifier Systems in an Agent based Simulation

      • Luis Miramontes Hercog
      Pages 143-166

    4. A Multi-Agent Model of the the UK Market in Electricity Generation

      • A. J. Bagnall
      Pages 167-181

    5. Exploring Organizational-Learning Oriented Classifier System in Real-World Problems

      • Keiki Takadama
      Pages 182-200

  6. Back Matter

    Pages 300-305
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Keywords

About this book

The field called Learning Classifier Systems is populated with romantics. Why shouldn't it be possible for computer programs to adapt, learn, and develop while interacting with their environments? In particular, why not systems that, like organic populations, contain competing, perhaps cooperating, entities evolving together? John Holland was one of the earliest scientists with this vision, at a time when so-called artificial intelligence was in its infancy and mainly concerned with preprogrammed systems that didn't learn. that, like organisms, had sensors, took Instead, Holland envisaged systems actions, and had rich self-generated internal structure and processing. In so doing he foresaw and his work prefigured such present day domains as reinforcement learning and embedded agents that are now displacing the older "standard Af' . One focus was what Holland called "classifier systems": sets of competing rule­ like "classifiers", each a hypothesis as to how best to react to some aspect of the environment--or to another rule. The system embracing such a rule "popu­ lation" would explore its available actions and responses, rewarding and rating the active rules accordingly. Then "good" classifiers would be selected and re­ produced, mutated and even crossed, a la Darwin and genetics, steadily and reliably increasing the system's ability to cope.

Editors and Affiliations

  • Faculty of Computing, Engineering & Mathematical Sciences, University of the West of England, Bristol, UK

    Larry Bull

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