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Simulation Optimization Through Regression or Kriging Metamodels

  • Jack P. C. KleijnenEmail author
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 833)

Abstract

This chapter surveys two methods for the optimization of real-world systems that are modelled through simulation. These methods use either linear regression or Kriging (Gaussian processes) metamodels. The metamodel guides the design of the experiment; this design fixes the input combinations of the simulation model. The linear-regression metamodel uses a sequence of local first-order and second-order polynomials—known as response surface methodology (RSM). Kriging models are global, but are re-estimated through sequential designs. “Robust” optimization may use RSM or Kriging, to account for uncertainty in simulation inputs.

Notes

Acknowledgements

I thank Thomas Bartz-Beielstein for his very useful comments on the first version of this chapter.

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Tilburg UniversityTilburgThe Netherlands

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