Generating sequential space-filling designs using genetic algorithms and Monte Carlo methods

Authors:
Karel Crombecq;Tom Dhaene
Affiliations:
University of Antwerp, Antwerp, Belgium;Ghent University, IBBT, Ghent, Belgium
Venue:
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
Year:
2010

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Abstract

In this paper, the authors compare a Monte Carlo method and an optimization-based approach using genetic algorithms for sequentially generating space-filling experimental designs. It is shown that Monte Carlo methods perform better than genetic algorithms for this specific problem.