Fuzzy Dynamical Genetic Programming in XCSF
This work addresses representation challenges in Learning Classifier Systems for researchers in evolutionary computation, but it appears incremental as it builds on existing Dynamical Genetic Programming methods.
The paper tackled the problem of designing effective representation schemes for Learning Classifier Systems by introducing a fuzzy Dynamical Genetic Programming approach within XCSF, using asynchronous Fuzzy Logic Networks to represent rules and achieving successful evolution to solve continuous-valued test problems.
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.