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30

SEAL
2010
Springer

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Generating Sequential Space-Filling Designs Using Genetic Algorithms and Monte Carlo Methods

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Generating Sequential Space-Filling Designs Using Genetic Algorithms and Monte Carlo Methods

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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.

Karel Crombecq, Tom Dhaene

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Genetic Algorithms | Machine Learning | Monte Carlo | Optimization-based Approach | SEAL 2010 |

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Added	30 Jan 2011
Updated	30 Jan 2011
Type	Journal
Year	2010
Where	SEAL
Authors	Karel Crombecq, Tom Dhaene

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