Fuzzy Dynamical Genetic Programming in XCSF

TL;DR

This paper explores integrating fuzzy dynamical genetic programming into XCSF to evolve complex, self-adaptive rule systems for solving continuous-valued problems, expanding the representation options in Learning Classifier Systems.

Contribution

It introduces the use of fuzzy DGP with asynchronous Fuzzy Logic Networks within XCSF, demonstrating self-adaptive evolution for continuous problem solving.

Findings

Successful application to continuous-valued test problems

Demonstration of self-adaptive ensemble evolution

Extension of representation schemes in Learning Classifier Systems

Abstract

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.