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Fuzzy-XCS: A Michigan genetic fuzzy system

Casillas, Jorge; Carse, Brian; Bull, Larry

Authors

Jorge Casillas

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



Abstract

The issue of finding fuzzy models with an interpretability as good as possible without decreasing the accuracy is one of the main research topics on genetic fuzzy systems. When they are used to perform online reinforcement learning by means of Michigan-style fuzzy rule systems, this issue becomes even more difficult. Indeed, rule generalization (description of state-action relationships with rules as compact as possible) has received a great attention in the nonfuzzy evolutionary learning field (e.g., XCS is the subject of extensive ongoing research). However, the same issue does not appear to have received a similar level of attention in the case of Michigan-style fuzzy rule systems. This may be due to the difficulty in extending the discrete-valued system operation to the continuous case. The intention of this contribution is to propose an approach to properly develop a fuzzy XCS system for single-step reinforcement problems. © 2007 IEEE.

Journal Article Type Article
Publication Date Aug 1, 2007
Journal IEEE Transactions on Fuzzy Systems
Print ISSN 1063-6706
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 15
Issue 4
Pages 536-550
APA6 Citation Casillas, J., Carse, B., & Bull, L. (2007). Fuzzy-XCS: A Michigan genetic fuzzy system. IEEE Transactions on Fuzzy Systems, 15(4), 536-550. https://doi.org/10.1109/TFUZZ.2007.900904
DOI https://doi.org/10.1109/TFUZZ.2007.900904
Keywords Continuous action, genetic fuzzy systems, Michigan-style learning classi´Čüer systems, reinforcement learning
Publisher URL http://dx.doi.org/10.1109/TFUZZ.2007.900904
Additional Information Additional Information : The main proposal of this paper is to extend the accuracy-based XCS learning classifier system to the fuzzy case, enabling the new fuzzy classifier system to operate using reinforcement learning with continuous valued inputs and outputs. This is a significant contribution since extending the discrete-valued, accuracy-based XCS to the fuzzy domain has long been recognized as a challenging problem. The representation and operators proposed in Fuzzy-XCS are designed to encourage optimal generalization in the evolved fuzzy rule base, providing easier scalability to higher dimensional spaces, faster inference and better linguistic interpretability.