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

Preen, Richard; Bull, Larry

Authors

Richard Preen Richard2.Preen@uwe.ac.uk
Research Fellow - Deep Evolutionary Learning

Lawrence Bull Larry.Bull@uwe.ac.uk
AHOD Research and Scholarship and Prof



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. © 2013 Springer-Verlag Berlin Heidelberg.

Journal Article Type Article
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
APA6 Citation Preen, R. J., Preen, R., & Bull, L. (2014). Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system. Soft Computing, 18(1), 153-167. https://doi.org/10.1007/s00500-013-1044-4
DOI https://doi.org/10.1007/s00500-013-1044-4
Keywords fuzzy logic networks, learning classifier systems, memory, random boolean networks, reinforcement learning, self-adaptation, XCSF
Publisher URL http://dx.doi.org/10.1007/s00500-013-1044-4
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