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

Karel Crombecq and Tom Dhaene UGent (2010) LECTURE NOTES IN COMPUTER SCIENCE. 6457. p.80-84
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.
Please use this url to cite or link to this publication:
author
organization
year
type
conference (proceedingsPaper)
publication status
published
subject
keyword
sequential design, Monte Carlo, surrogate modelling, active learning, genetic algorithm, space-filling
in
LECTURE NOTES IN COMPUTER SCIENCE
Lect. Notes Comput. Sci.
editor
K Deb, A Bhattacharya, N Chakraborti, P Chakroborty, S Das, J Dutta, SK Gupta, A Jain, V Aggarwal, J Branke, SJ Louis and KC Tan
volume
6457
issue title
Simulated evolution and learning
pages
80 - 84
publisher
Springer
place of publication
Berlin, Germany
conference name
8th International conference on Simulated Evolution And Learning
conference location
Kanpur, India
conference start
2010-12-01
conference end
2010-12-04
Web of Science type
Proceedings Paper
Web of Science id
000289185200008
ISSN
0302-9743
ISBN
9783642172977
DOI
10.1007/978-3-642-17298-4_8
language
English
UGent publication?
yes
classification
P1
copyright statement
I have transferred the copyright for this publication to the publisher
id
1140796
handle
http://hdl.handle.net/1854/LU-1140796
date created
2011-02-07 11:35:37
date last changed
2017-01-02 09:52:34
@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},
}

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.