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

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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.
Keywords
sequential design, Monte Carlo, surrogate modelling, active learning, genetic algorithm, space-filling

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Chicago
Crombecq, Karel, and Tom Dhaene. 2010. “Generating Sequential Space-filling Designs Using Genetic Algorithms and Monte Carlo Methods.” In Lecture Notes in Computer Science, ed. K Deb, A Bhattacharya, N Chakraborti, P Chakroborty, S Das, J Dutta, SK Gupta, et al., 6457:80–84. Berlin, Germany: Springer.
APA
Crombecq, Karel, & Dhaene, T. (2010). Generating sequential space-filling designs using genetic algorithms and Monte Carlo methods. In K. Deb, A. Bhattacharya, N. Chakraborti, P. Chakroborty, S. Das, J. Dutta, S. Gupta, et al. (Eds.), LECTURE NOTES IN COMPUTER SCIENCE (Vol. 6457, pp. 80–84). Presented at the 8th International conference on Simulated Evolution And Learning, Berlin, Germany: Springer.
Vancouver
1.
Crombecq K, Dhaene T. Generating sequential space-filling designs using genetic algorithms and Monte Carlo methods. In: Deb K, Bhattacharya A, Chakraborti N, Chakroborty P, Das S, Dutta J, et al., editors. LECTURE NOTES IN COMPUTER SCIENCE. Berlin, Germany: Springer; 2010. p. 80–4.
MLA
Crombecq, Karel, and Tom Dhaene. “Generating Sequential Space-filling Designs Using Genetic Algorithms and Monte Carlo Methods.” Lecture Notes in Computer Science. Ed. K Deb et al. Vol. 6457. Berlin, Germany: Springer, 2010. 80–84. Print.
@inproceedings{1140796,
  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.},
  author       = {Crombecq, Karel and Dhaene, Tom},
  booktitle    = {LECTURE NOTES IN COMPUTER SCIENCE},
  editor       = {Deb, K and Bhattacharya, A and Chakraborti, N and Chakroborty, P and Das, S and Dutta, J and Gupta, SK and Jain, A and Aggarwal, V and Branke, J and Louis, SJ and Tan, KC},
  isbn         = {9783642172977},
  issn         = {0302-9743},
  keyword      = {sequential design,Monte Carlo,surrogate modelling,active learning,genetic algorithm,space-filling},
  language     = {eng},
  location     = {Kanpur, India},
  pages        = {80--84},
  publisher    = {Springer},
  title        = {Generating sequential space-filling designs using genetic algorithms and Monte Carlo methods},
  url          = {http://dx.doi.org/10.1007/978-3-642-17298-4\_8},
  volume       = {6457},
  year         = {2010},
}

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