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

Preen, Richard; Bull, Larry


Richard Preen
Senior Research Fellow in Machine Learning

Lawrence Bull
School Director (Research & Enterprise) and Professor


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. © 2013 Springer-Verlag Berlin Heidelberg.


Preen, R., & Bull, L. (2014). Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system. Soft Computing, 18(1), 153-167.

Journal Article Type Article
Online Publication Date Apr 17, 2013
Publication Date Jan 1, 2014
Journal Soft Computing
Print ISSN 1432-7643
Electronic ISSN 1433-7479
Publisher Springer (part of Springer Nature)
Peer Reviewed Peer Reviewed
Volume 18
Issue 1
Pages 153-167
Keywords fuzzy logic networks, learning classifier systems, memory, random boolean networks, reinforcement learning, self-adaptation, XCSF
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