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. © 2010 Springer-Verlag.
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APA
Crombecq, K., & Dhaene, T. (2010). Generating sequential space-filling designs using genetic algorithms and Monte Carlo methods. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6457 LNCS, pp. 80–84). https://doi.org/10.1007/978-3-642-17298-4_8
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