Discrete dynamical genetic programming in XCS

Preen, Richard; Bull, Larry

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.

• Presentation Conference Type: Conference Paper (unpublished)
• Conference Name: 11th Annual conference on Genetic and evolutionary computation
• Start Date: Jul 8, 2009
• End Date: Jul 12, 2009
• Publication Date: Jul 8, 2009
• Peer Reviewed: Peer Reviewed
• Pages: 1299-1306
• Keywords: XCS, discrete dynamical genetic programming
• Public URL: https://uwe-repository.worktribe.com/output/1435837
• Publisher URL: http://dx.doi.org/10.1145/1569901.1570075
• Additional Information: Title of Conference or Conference Proceedings : 11th Annual conference on Genetic and evolutionary computation (GECCO '09)