Article

Extending XCSF beyond linear approximation

Authors:

Pier Luca Lanzi,

Daniele Loiacono,

Stewart W. Wilson,

David E. GoldbergAuthors Info & Claims

GECCO '05: Proceedings of the 7th annual conference on Genetic and evolutionary computation

Pages 1827 - 1834

https://doi.org/10.1145/1068009.1068319

Published: 25 June 2005 Publication History

Abstract

XCSF is the extension of XCS in which classifier prediction is computed as a linear combination of classifier inputs and a weight vector associated to each classifier. XCSF can exploit classifiers' computable prediction to evolve accurate piecewise linear approximations of functions. In this paper, we take XCSF one step further and show how XCSF can be easily extended to allow polynomial approximations. We test the extended version of XCSF on various approximation problems and show that quadratic/cubic approximations can be used to significantly improve XCSF's generalization capabilities.

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Cited By

Schönberner CTomforde SLi XHandl J(2024)XCS: Is Covering All You Need?Proceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3664146(1788-1796)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638530.3664146
Chen QXue BBrowne WZhang M(2023)Evolutionary Regression and ModellingHandbook of Evolutionary Machine Learning10.1007/978-981-99-3814-8_5(121-149)Online publication date: 2-Nov-2023
https://doi.org/10.1007/978-981-99-3814-8_5
Schönberner CTomforde S(2022)Deep Reinforcement Learning with a Classifier System – First StepsArchitecture of Computing Systems10.1007/978-3-031-21867-5_17(256-270)Online publication date: 14-Dec-2022
https://doi.org/10.1007/978-3-031-21867-5_17
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Index Terms

Extending XCSF beyond linear approximation
1. Mathematics of computing
  1. Mathematical analysis
    1. Functional analysis
      1. Approximation
2. Theory of computation

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Published In

cover image ACM Conferences

GECCO '05: Proceedings of the 7th annual conference on Genetic and evolutionary computation

June 2005

2272 pages

ISBN:1595930108

DOI:10.1145/1068009

Editor:
Hans-Georg Beyer
Vorarlberg University of Applied Sciences, Austria
,
General Chair:
Una-May O'Reilly
CSAIL, MIT

Copyright © 2005 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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Publication History

Published: 25 June 2005

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June 25 - 29, 2005

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Schönberner CTomforde SLi XHandl J(2024)XCS: Is Covering All You Need?Proceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3664146(1788-1796)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638530.3664146
Chen QXue BBrowne WZhang M(2023)Evolutionary Regression and ModellingHandbook of Evolutionary Machine Learning10.1007/978-981-99-3814-8_5(121-149)Online publication date: 2-Nov-2023
https://doi.org/10.1007/978-981-99-3814-8_5
Schönberner CTomforde S(2022)Deep Reinforcement Learning with a Classifier System – First StepsArchitecture of Computing Systems10.1007/978-3-031-21867-5_17(256-270)Online publication date: 14-Dec-2022
https://doi.org/10.1007/978-3-031-21867-5_17
Hansmeier TKaufmann PPlatzner MCoello Coello C(2020)An adaption mechanism for the error threshold of XCSFProceedings of the 2020 Genetic and Evolutionary Computation Conference Companion10.1145/3377929.3398106(1756-1764)Online publication date: 8-Jul-2020
https://dl.acm.org/doi/10.1145/3377929.3398106
Bishop JGallagher M(2020)Optimality-Based Analysis of XCSF Compaction in Discrete Reinforcement LearningParallel Problem Solving from Nature – PPSN XVI10.1007/978-3-030-58115-2_33(471-484)Online publication date: 2-Sep-2020
https://doi.org/10.1007/978-3-030-58115-2_33
Stein AAuger AStützle T(2019)Learning classifier systemsProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3319619.3323393(747-769)Online publication date: 13-Jul-2019
https://dl.acm.org/doi/10.1145/3319619.3323393
Stein AMenssen SHähner JTakadama K(2018)What about interpolation?Proceedings of the Genetic and Evolutionary Computation Conference10.1145/3205455.3205599(537-544)Online publication date: 2-Jul-2018
https://dl.acm.org/doi/10.1145/3205455.3205599
Matsushima HTakadama K(2017)Exemplar-Based Learning Classifier System with Dynamic Matching Range for Imbalanced DataJournal of Advanced Computational Intelligence and Intelligent Informatics10.20965/jaciii.2017.p086821:5(868-875)Online publication date: 20-Sep-2017
https://doi.org/10.20965/jaciii.2017.p0868
Saremi OPanahi MSabzehzar A(2015)An Improved Continuous-Action Extended Classifier Systems for Function ApproximationProcedia Computer Science10.1016/j.procs.2015.09.16061(361-366)Online publication date: 2015
https://doi.org/10.1016/j.procs.2015.09.160
Lanzi PLoiacono D(2015)XCSF with tile coding in discontinuous action-value landscapesEvolutionary Intelligence10.1007/s12065-015-0129-78:2-3(117-132)Online publication date: 11-Apr-2015
https://doi.org/10.1007/s12065-015-0129-7
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