MILCS: a mutual information learning classifier system

Authors:
R. E. Smith;Max Kun Jiang
Affiliations:
University Colege London, London, United Kingdom;University Colege London, London, United Kingdom
Venue:
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Year:
2007

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Abstract

This paper introduces a new variety of learning classifier system (LCS), called MILCS, which utilizes mutual information as fitness feedback. Unlike most LCSs, MILCS is specifically designed for supervised learning. MILCS's design draws on an analogy to the structural learning approach of cascade correlation networks. We present preliminary results, and contrast them to results from XCS. We discuss the explanatory power of the resulting rule sets, and introduce a new technique for visualizing explanatory power. Final comments include future directions for this research, including investigations in neural networks and other systems.