Using expert knowledge to guide covering and mutation in a michigan style learning classifier system to detect epistasis and heterogeneity

Authors:
Ryan J. Urbanowicz;Delaney Granizo-Mackenzie;Jason H. Moore
Affiliations:
Computational Genetics Laboratory, Department of Genetics, Dartmouth Medical School, Lebanon, NH;Computational Genetics Laboratory, Department of Genetics, Dartmouth Medical School, Lebanon, NH;Computational Genetics Laboratory, Department of Genetics, Dartmouth Medical School, Lebanon, NH
Venue:
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Year:
2012

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Abstract

Learning Classifier Systems (LCSs) are a unique brand of multifaceted evolutionary algorithms well suited to complex or heterogeneous problem domains. One such domain involves data mining within genetic association studies which investigate human disease. Previously we have demonstrated the ability of Michigan-style LCSs to detect genetic associations in the presence of two complicating phenomena: epistasis and genetic heterogeneity. However, LCSs are computationally demanding and problem scaling is a common concern. The goal of this paper was to apply and evaluate expert knowledge-guided covering and mutation operators within an LCS algorithm. Expert knowledge, in the form of Spatially Uniform ReliefF (SURF) scores, was incorporated to guide learning towards regions of the problem domain most likely to be of interest. This study demonstrates that expert knowledge can improve learning efficiency in the context of a Michigan-style LCS.