Discrete dynamical genetic programming in XCS

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. Categories and Subject Descriptors I.2.6 [Artificial Intelligence]: Learning – knowledge acquisition, parameter learning. General Terms Experimentation. Keywords Learning Classifier Systems, XCS, Random Boolean Networks, Reinforcement Learning, Self-Adaptation.

• Type: Conference
• Year: 2009
• Where: GECCO
• Authors: Richard Preen, Larry Bull
• Keywords: Artificial Intelligence, Discrete Dynamical Systems, GECCO 2009, Learning Classifier Systems, Random Boolean Networks