Parameter sweeps for exploring GP parameters

Authors:
Michael E. Samples;Jason M. Daida;Matt Byom;Matt Pizzimenti
Affiliations:
The University of Michigan, Ann Arbor, MI;The University of Michigan, Ann Arbor, MI;The University of Michigan, Ann Arbor, MI;The University of Michigan, Ann Arbor, MI
Venue:
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Year:
2005

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Abstract

This paper describes our procedure and a software application for conducting large parameter sweep experiments in genetic and evolutionary computation research. Both procedure and software allows a researcher to examine multivariate nonlinearities that are common in genetic and evolutionary computation. Experiments of this nature are well suited to distributed computing environments (such as Grids and clusters) and we present an automated system for conducting parameter sweep experiments on heterogeneous networks. Emphasis is placed on experimental sampling, distributed robustness, and data analysis. The parameter sweep experimental procedure is easily applicable to any experiment involving computer simulations but is particularly well suited for evolutionary computation experiments.