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Efficient Global Optimization of Expensive Black-Box Functions

Jones, Donald R., Schonlau, Matthias, Welch, William J.·1998·Journal of Global Optimization

DOI 10.1023/a:1008306431147

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Numerical recipes in Pascal: the art of scientific computing
(1990) · Choice Reviews Online · doi:10.5860/choice.27-5158 · Cited by 12,040
STATISTICS FOR SPATIAL DATA
Cressie, N. (1992) · Terra Nova · doi:10.1111/j.1365-3121.1992.tb00605.x · Cited by 9,139
A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code
McKay, M. D., Beckman, R. J., & Conover, W. J. (2000) · Technometrics · doi:10.2307/1271432 · Cited by 8,270
Numerical Recipes in FORTRAN
Vetterling, W. T., Teukolsky, S. A., Press, W. H., & Flannery, B. P. (1988) · Cited by 7,701
A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code
McKay, M. D., Beckman, R. J., & Conover, W. J. (1979) · Technometrics · doi:10.1080/00401706.1979.10489755 · Cited by 7,463
Design and Analysis of Computer Experiments
Sacks, J., Welch, W. J., Mitchell, T. J., & Wynn, H. P. (1989) · Statistical Science · doi:10.1214/ss/1177012413 · Cited by 6,960
Self-citation
Statistics for Experimenters
Addelman, S., Box, G. E. P., Hunter, W. G., & Hunter, J. S. (1979) · Technometrics · doi:10.2307/1267766 · Cited by 6,753
Statistics for Spatial Data
Cressie, N. A. C. (1993) · Wiley Series in Probability and Statistics · doi:10.1002/9781119115151 · Cited by 6,507
5. Statistics for Spatial Data
Rees, M., & Cressie, N. (1993) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.2307/2982871 · Cited by 4,983
Principles of geostatistics
Matheron, G. (1963) · Economic Geology · doi:10.2113/gsecongeo.58.8.1246 · Cited by 4,851

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Dixon, L. C. W. and Szego, G. P. (1978), The global optimisation problem: an introduction, in L. C. W. Dixon and G. P. Szego (eds.), Towards Global Optimisation, Vol. 2, pp. 1–15. North Holland, Amsterdam.
d optimization when probes are expensive: the GROPE algorithm. Proceedings of the 1992 IEEE International Conference on Systems, Man, and Cybernetics, Vol. 1, pp. 577-582, Chicago.
Mockus, J., Tiesis, V. and Zilinskas, A. (1978), The application of Bayesian methods for seeking the extremum, in L. C. W. Dixon and G. P. Szego (eds.), Towards Global Optimisation, Vol.2, pp. 117–129. North Holland, Amsterdam.
Sacks, J. and Ylvisaker, D. (1970), Statistical designs and integral approximation, in R. Pyke (ed.), Proceedings of the Twelfth Biennial Seminar of the Canadian Mathematical Congress, pp. 115–136, Canadian Mathematical Congress, Montreal.

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Authors: Jones, D. R., Schonlau, M., & Welch, W. J.
Affiliations: General Motors (United States) (US)
National Institute of Statistical Sciences (US)
University of Waterloo (CA)
ORCID: Jones, Donald R.
Schonlau, Matthias
Venue: Journal of Global Optimization
Publisher: Springer Science and Business Media LLC
Published: 1998-12
Volume: 13
Issue: 4
Pages: 455-492
ISSN: 0925-5001 (ISSN-L)
1573-2916
DOI: 10.1023/a:1008306431147
OpenAlex: W1510052597
Cited by: 8,070
References: 34
Citations by year: 2026: 730 · 2025: 661 · 2024: 601 · 2023: 599 · 2022: 636 · 2021: 659 · 2020: 612 · 2019: 548 · 2018: 458 · 2017: 369 · 2016: 370 · 2015: 284 · 2014: 255 · 2013: 219 · 2012: 215
FWCI: 7.84
Type: journal article
Language: en
Open access: closed
Licence: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
Locations: Journal of Global Optimization (publishedVersion)
Topics: Physical Sciences › Mathematics › Numerical Analysis › Advanced Optimization Algorithms Research
Physical Sciences › Computer Science › Computational Theory and Mathematics › Advanced Multi-Objective Optimization Algorithms
Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
Keywords: expensive black-box functions, global optimization, response surface methodology, surrogate models, stopping rules, exploration-exploitation balance, prediction error, engineering design optimization

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Efficient Global Optimization of Expensive Black-Box Functions

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