article

Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system

Authors:

Richard J. Preen,

Larry BullAuthors Info & Claims

Soft Computing - A Fusion of Foundations, Methodologies and Applications, Volume 18, Issue 1

Pages 153 - 167

https://doi.org/10.1007/s00500-013-1044-4

Published: 01 January 2014 Publication History

Abstract

A number of representation schemes have been presented for use within learning classifier systems, ranging from binary encodings to neural networks. This paper presents results from an investigation into using discrete and fuzzy dynamical system representations within the XCSF learning classifier system. In particular, asynchronous random Boolean networks are used to represent the traditional condition-action production system rules in the discrete case and asynchronous fuzzy logic networks in the continuous-valued case. It is shown possible to use self-adaptive, open-ended evolution to design an ensemble of such dynamical systems within XCSF to solve a number of well-known test problems.

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Cited By

Preen RWilson SBull L(2021)Autoencoding With a Classifier SystemIEEE Transactions on Evolutionary Computation10.1109/TEVC.2021.307932025:6(1079-1090)Online publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1109/TEVC.2021.3079320
Shafi KAbbass H(2017)A Survey of Learning Classifier Systems in Games [Review Article]IEEE Computational Intelligence Magazine10.1109/MCI.2016.262767012:1(42-55)Online publication date: 1-Feb-2017
https://dl.acm.org/doi/10.1109/MCI.2016.2627670
Howard DBull LLanzi P(2016)A Cognitive Architecture Based on a Learning Classifier System with Spiking ClassifiersNeural Processing Letters10.1007/s11063-015-9451-444:1(125-147)Online publication date: 1-Aug-2016
https://dl.acm.org/doi/10.1007/s11063-015-9451-4
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Index Terms

Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system
1. Computing methodologies
  1. Machine learning
2. Theory of computation
  1. Logic

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cover image Soft Computing - A Fusion of Foundations, Methodologies and Applications

Soft Computing - A Fusion of Foundations, Methodologies and Applications Volume 18, Issue 1

January 2014

198 pages

ISSN:1432-7643

EISSN:1433-7479

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Copyright © Copyright © 2014 Springer-Verlag Berlin Heidelberg.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 January 2014

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Cited By

Preen RWilson SBull L(2021)Autoencoding With a Classifier SystemIEEE Transactions on Evolutionary Computation10.1109/TEVC.2021.307932025:6(1079-1090)Online publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1109/TEVC.2021.3079320
Shafi KAbbass H(2017)A Survey of Learning Classifier Systems in Games [Review Article]IEEE Computational Intelligence Magazine10.1109/MCI.2016.262767012:1(42-55)Online publication date: 1-Feb-2017
https://dl.acm.org/doi/10.1109/MCI.2016.2627670
Howard DBull LLanzi P(2016)A Cognitive Architecture Based on a Learning Classifier System with Spiking ClassifiersNeural Processing Letters10.1007/s11063-015-9451-444:1(125-147)Online publication date: 1-Aug-2016
https://dl.acm.org/doi/10.1007/s11063-015-9451-4
Zang ZLi DWang J(2015)Learning classifier systems with memory condition to solve non-Markov problemsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-014-1357-y19:6(1679-1699)Online publication date: 1-Jun-2015
https://dl.acm.org/doi/10.1007/s00500-014-1357-y

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