Expected Improvement in Efficient Global Optimization Through Bootstrapped Kriging
CentER Discussion Paper Series No. 2011-015 (Revision of 2010-062)
20 Pages Posted: 21 Feb 2011
There are 2 versions of this paper
Expected Improvement in Efficient Global Optimization Through Bootstrapped Kriging
Expected Improvement in Efficient Global Optimization Through Bootstrapped Kriging
Date Written: February 11, 2011
Abstract
This 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 unbiased 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.
Keywords: Simulation, Optimization, Kriging, Bootstrap
JEL Classification: C0, C1, C9, C15, C44, C61
Suggested Citation: Suggested Citation
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