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arXiv · 1204.4200

Discrete Dynamical Genetic Programming in XCS

Richard J. Preen·Larry Bull
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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.

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BibTeXRIS

Richard J. Preen, Larry Bull. 2012-04-18. Discrete Dynamical Genetic Programming in XCS. https://doi.org/10.1145/1569901.1570075

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