Discrete Dynamical Genetic Programming in XCS

TL;DR

This paper explores using discrete dynamical systems, specifically asynchronous random Boolean networks, within the XCS Learning Classifier System to enable self-adaptive evolution for solving standard test problems.

Contribution

It introduces a novel representation scheme using asynchronous random Boolean networks in XCS, demonstrating self-adaptive evolution for problem-solving.

Findings

Successful application of Boolean networks in XCS for test problems

Demonstration of open-ended evolution in system design

Enhanced flexibility of classifier representations

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 a discrete dynamical system representation within the XCS Learning Classifier System. In particular, asynchronous random Boolean 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 discrete dynamical systems within XCS to solve a number of well-known test problems.