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

TL;DR

This paper explores the integration of discrete and fuzzy dynamical systems, specifically asynchronous Boolean and fuzzy logic networks, into the XCSF learning classifier system to enhance its problem-solving capabilities.

Contribution

It introduces the use of self-adaptive evolution to design ensembles of dynamical systems within XCSF for solving complex test problems.

Findings

Successful application of Boolean networks in discrete representations

Effective use of fuzzy logic networks for continuous-valued problems

Demonstrated adaptability of the evolved dynamical systems

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.