A simple multi-core parallelization strategy for learning classifier system evaluation

Authors:
James Rudd;Jason Moore;Ryan Urbanowicz
Affiliations:
Dartmouth College, Lebanon, NH, USA;Dartmouth College, Lebanon, NH, USA;Dartmouth College, Lebanon, NH, USA
Venue:
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Year:
2013

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Abstract

Permutation strategies for statistically evaluating the significance of predictions and patterns identified within learning classifier systems (LCSs) have only appeared since 2012. While already considered to be computationally expensive algorithms, a permutation testing based approach to determining statistical significance has the potential to be many times more demanding. One area of LCS research which has become both feasible and popularized in recent years is the adoption of parallelization strategies. In the present study we explore the simple benefits of parallelizing a set of LCS analyses in an attempt to make the completion of a permutation test with cross validation more feasible on a single multi-core workstation. We test our python implementation of this strategy in the context of a simulated complex genetic epidemiological data mining problem. Our evaluations indicate that on Windows 7 computers, as long as the number of concurrent processes does not exceed the number of CPU cores, the speedup achieved is approximately linear.