A hybrid sequential sampling based metamodelling approach for high dimensional problems
- Author
- Selvakumar Ulaganathan (UGent) , Ivo Couckuyt (UGent) , Tom Dhaene (UGent) , Eric Laermans (UGent) and Joris Degroote (UGent)
- Organization
- Abstract
- High Dimensional Model Representation (HDMR) offers efficient ways to approximate computation-intensive high-dimensional black-box functions. The distinctive nature of HDMR allows a high-dimensional problem to be decomposed into a low-dimensional function or a combination of various low-dimensional functions, thus making it more attractive than other popular metamodelling approaches such as Kriging, Radial basis function, etc. However, the computational cost of HDMR is still a bottleneck for high-dimensional problems. In this work, a hybrid sequential sampling based Kriging metamodelling technique is integrated with HDMR to improve the computational efficiency of HDMR for high-dimensional problems. The performance of the proposed metamodelling approach for high-dimensional problems is validated with various benchmark mathematical problems of a wide scope of dimensionalities.
- Keywords
- COMPUTER EXPERIMENTS, DESIGN, REGRESSION, TOOLBOX
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8506088
- MLA
- Ulaganathan, Selvakumar, et al. “A Hybrid Sequential Sampling Based Metamodelling Approach for High Dimensional Problems.” 2016 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), Ieee, 2016, pp. 1917–23.
- APA
- Ulaganathan, S., Couckuyt, I., Dhaene, T., Laermans, E., & Degroote, J. (2016). A hybrid sequential sampling based metamodelling approach for high dimensional problems. 2016 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 1917–1923. New york: Ieee.
- Chicago author-date
- Ulaganathan, Selvakumar, Ivo Couckuyt, Tom Dhaene, Eric Laermans, and Joris Degroote. 2016. “A Hybrid Sequential Sampling Based Metamodelling Approach for High Dimensional Problems.” In 2016 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 1917–23. New york: Ieee.
- Chicago author-date (all authors)
- Ulaganathan, Selvakumar, Ivo Couckuyt, Tom Dhaene, Eric Laermans, and Joris Degroote. 2016. “A Hybrid Sequential Sampling Based Metamodelling Approach for High Dimensional Problems.” In 2016 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 1917–1923. New york: Ieee.
- Vancouver
- 1.Ulaganathan S, Couckuyt I, Dhaene T, Laermans E, Degroote J. A hybrid sequential sampling based metamodelling approach for high dimensional problems. In: 2016 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC). New york: Ieee; 2016. p. 1917–23.
- IEEE
- [1]S. Ulaganathan, I. Couckuyt, T. Dhaene, E. Laermans, and J. Degroote, “A hybrid sequential sampling based metamodelling approach for high dimensional problems,” in 2016 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), Vancouver, CANADA, 2016, pp. 1917–1923.
@inproceedings{8506088,
abstract = {{High Dimensional Model Representation (HDMR) offers efficient ways to approximate computation-intensive high-dimensional black-box functions. The distinctive nature of HDMR allows a high-dimensional problem to be decomposed into a low-dimensional function or a combination of various low-dimensional functions, thus making it more attractive than other popular metamodelling approaches such as Kriging, Radial basis function, etc. However, the computational cost of HDMR is still a bottleneck for high-dimensional problems. In this work, a hybrid sequential sampling based Kriging metamodelling technique is integrated with HDMR to improve the computational efficiency of HDMR for high-dimensional problems. The performance of the proposed metamodelling approach for high-dimensional problems is validated with various benchmark mathematical problems of a wide scope of dimensionalities.}},
author = {{Ulaganathan, Selvakumar and Couckuyt, Ivo and Dhaene, Tom and Laermans, Eric and Degroote, Joris}},
booktitle = {{2016 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)}},
isbn = {{978-1-5090-0622-9}},
keywords = {{COMPUTER EXPERIMENTS,DESIGN,REGRESSION,TOOLBOX}},
language = {{eng}},
location = {{Vancouver, CANADA}},
pages = {{1917--1923}},
publisher = {{Ieee}},
title = {{A hybrid sequential sampling based metamodelling approach for high dimensional problems}},
year = {{2016}},
}