Expected Improvement in Efficient Global Optimization Through Bootstrapped Kriging - Replaced by CentER DP 2011-015
AbstractThis paper uses a sequentialized experimental design to select simulation input com- binations 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 paper adapts the classic "ex- pected improvement" (EI) in "efficient global optimization" (EGO) through the introduction of an unbiased estimator of the Kriging predictor variance; this estima- tor uses parametric bootstrapping. Classic EI and bootstrapped EI are compared through four popular test functions, including the six-hump camel-back and two 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.
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Bibliographic InfoPaper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number 2010-62.
Date of creation: 2010
Date of revision:
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Web page: http://center.uvt.nl
Simulation; Optimization; Kriging; Bootstrap;
Find related papers by JEL classification:
- C0 - Mathematical and Quantitative Methods - - General
- C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
- C9 - Mathematical and Quantitative Methods - - Design of Experiments
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- NEP-ALL-2010-08-21 (All new papers)
- NEP-CMP-2010-08-21 (Computational Economics)
- NEP-ECM-2010-08-21 (Econometrics)
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