Expected improvement in efficient global optimization through bootstrapped kriging
AbstractThis article uses a sequentialized experimental design to select simulation input combinations for global optimization, based on Kriging (also called Gaussian process or spatial correlation modeling); this Kriging is used to analyze the input/output data of the simulation model (computer code). This design and analysis adapt the classic “expected improvement” (EI) in “efficient global optimization” (EGO) through the introduction of an improved estimator of the Kriging predictor variance; this estimator uses parametric bootstrapping. Classic EI and bootstrapped EI are compared through various test functions, including the six-hump camel-back and several Hartmann functions. These empirical results demonstrate that in some applications bootstrapped EI finds the global optimum faster than classic EI does; in general, however, the classic EI may be considered to be a robust global optimizer. Copyright The Author(s) 2012
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by Springer in its journal Journal of Global Optimization.
Volume (Year): 54 (2012)
Issue (Month): 1 (September)
Contact details of provider:
Web page: http://www.springer.com/business/operations+research/journal/10898
Other versions of this item:
- Van Nieuwenhuyse, Inneke & Kleijnen, Jack & van Beers, Wim, 2010. "Expected improvement in efficient global optimization through bootstrapped kriging," Open Access publications from Katholieke Universiteit Leuven urn:hdl:123456789/278222, Katholieke Universiteit Leuven.
- Kleijnen, Jack & van Beers, Wim & Van Nieuwenhuyse, Inneke, 2012. "Expected improvement in efficient global optimization through bootstrapped kriging," Open Access publications from Katholieke Universiteit Leuven urn:hdl:123456789/310611, Katholieke Universiteit Leuven.
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Kleijnen, Jack P.C. & van Beers, Wim & Van Nieuwenhuyse, Inneke, 2010.
"Constrained optimization in expensive simulation: novel approach,"
Open Access publications from Katholieke Universiteit Leuven
urn:hdl:123456789/230053, Katholieke Universiteit Leuven.
- Kleijnen, Jack P.C. & Beers, Wim van & Nieuwenhuyse, Inneke van, 2010. "Constrained optimization in expensive simulation: Novel approach," European Journal of Operational Research, Elsevier, vol. 202(1), pages 164-174, April.
- Kleijnen, Jack P.C. & Beers, W.C.M. van & Nieuwenhuyse, I. van, 2010. "Constrained optimization in simulation: A novel approach," Open Access publications from Tilburg University urn:nbn:nl:ui:12-3583585, Tilburg University.
- Markus Abt, 1999. "Estimating the Prediction Mean Squared Error in Gaussian Stochastic Processes with Exponential Correlation Structure," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics & Finnish Statistical Society & Norwegian Statistical Association & Swedish Statistical Association, vol. 26(4), pages 563-578.
- Kleijnen, J.P.C., 2008. "Review of the book [Design and Analysis of Simulation Experiments]," Open Access publications from Tilburg University urn:nbn:nl:ui:12-4379049, Tilburg University.
- Kleijnen, Jack P.C. & Mehdad, E., 2012. "Kriging in Multi-response Simulation, including a Monte Carlo Laboratory," Discussion Paper 2012-039, Tilburg University, Center for Economic Research.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Guenther Eichhorn) or (Christopher F Baum).
If references are entirely missing, you can add them using this form.