research-article

On the scalability of XCS(F)

Authors:

Patrick O. Stalph,

Martin V. Butz,

David E. Goldberg,

Xavier LloràAuthors Info & Claims

GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation

Pages 1315 - 1322

https://doi.org/10.1145/1569901.1570077

Published: 08 July 2009 Publication History

Abstract

Many successful applications have proven the potential of Learning Classifier Systems and the XCS classifier system in particular in datamining, reinforcement learning, and function approximation tasks. Recent research has shown that XCS is a highly flexible system, which can be adapted to the task at hand by adjusting its condition structures, learning operators, and prediction mechanisms. However, fundamental theory concerning the scalability of XCS dependent on these enhancements and problem difficulty is still rather sparse and mainly restricted to boolean function problems. In this article we developed a learning scalability theory for XCSF---the XCS system applied to real-valued function approximation problems. We determine crucial dependencies on functional properties and on the developed solution representation and derive a theoretical scalability model out of these constraints. The theoretical model is verified with empirical evidence. That is, we show that given a particular problem difficulty and particular representational constraints XCSF scales optimally. In consequence, we discuss the importance of appropriate prediction and condition structures regarding a given problem and show that scalability properties can be improved by polynomial orders, given an appropriate, problem-suitable representation.

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Cited By

Pätzel DStein AHähner JAuger AStützle T(2019)A survey of formal theoretical advances regarding XCSProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3319619.3326848(1295-1302)Online publication date: 13-Jul-2019
https://dl.acm.org/doi/10.1145/3319619.3326848
Debie EShafi KLokan CMerrick K(2013)Performance analysis of rough set ensemble of learning classifier systems with differential evolution based rule discoveryEvolutionary Intelligence10.1007/s12065-013-0093-z6:2(109-126)Online publication date: 9-Oct-2013
https://doi.org/10.1007/s12065-013-0093-z
Abedini MKirley M(2012)An enhanced XCS rule discovery module using feature rankingInternational Journal of Machine Learning and Cybernetics10.1007/s13042-012-0085-94:3(173-187)Online publication date: 18-Mar-2012
https://doi.org/10.1007/s13042-012-0085-9
Show More Cited By

Index Terms

On the scalability of XCS(F)
1. Mathematics of computing
  1. Mathematical analysis
    1. Functional analysis
      1. Approximation
    2. Numerical analysis
2. Theory of computation
  1. Design and analysis of algorithms
    1. Approximation algorithms analysis

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cover image ACM Conferences

GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation

July 2009

2036 pages

ISBN:9781605583259

DOI:10.1145/1569901

General Chair:
Franz Rothlauf
University of Mainz, Germany

Copyright © 2009 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 08 July 2009

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GECCO09: Genetic and Evolutionary Computation Conference

July 8 - 12, 2009

Québec, Montreal, Canada

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Cited By

Pätzel DStein AHähner JAuger AStützle T(2019)A survey of formal theoretical advances regarding XCSProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3319619.3326848(1295-1302)Online publication date: 13-Jul-2019
https://dl.acm.org/doi/10.1145/3319619.3326848
Debie EShafi KLokan CMerrick K(2013)Performance analysis of rough set ensemble of learning classifier systems with differential evolution based rule discoveryEvolutionary Intelligence10.1007/s12065-013-0093-z6:2(109-126)Online publication date: 9-Oct-2013
https://doi.org/10.1007/s12065-013-0093-z
Abedini MKirley M(2012)An enhanced XCS rule discovery module using feature rankingInternational Journal of Machine Learning and Cybernetics10.1007/s13042-012-0085-94:3(173-187)Online publication date: 18-Mar-2012
https://doi.org/10.1007/s13042-012-0085-9
Abedini MKirley M(2010)A multiple population XCS: Evolving condition-action rules based on feature space partitionsIEEE Congress on Evolutionary Computation10.1109/CEC.2010.5586521(1-8)Online publication date: Jul-2010
https://doi.org/10.1109/CEC.2010.5586521

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