Classifiers that approximate functions
Natural Computing: an international journal
XCSF with computed continuous action
Proceedings of the 9th annual conference on Genetic and evolutionary computation
QFCS: A Fuzzy LCS in Continuous Multi-step Environments with Continuous Vector Actions
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Discrete dynamical genetic programming in XCS
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Three architectures for continuous action
IWLCS'03-05 Proceedings of the 2003-2005 international conference on Learning classifier systems
XCSR with computed continuous action
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
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A number of representation schemes have been presented for use within Learning Classifier Systems, ranging from binary encodings to Neural Networks, and more recently Dynamical Genetic Programming (DGP). This paper presents results from an investigation into using a fuzzy DGP representation within the XCSF Learning Classifier System. In particular, asynchronous Fuzzy Logic Networks are used to represent the traditional condition-action production system rules. It is shown possible to use self-adaptive, open-ended evolution to design an ensemble of such fuzzy dynamical systems within XCSF to solve several well-known continuous-valued test problems.