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Accueil | Français » Publications » Articles scientifiques » Conditional optimization of a noisy function using a kriging meta (...)

Conditional optimization of a noisy function using a kriging meta model

Diariétou Sambakhé1,2,3,4,5,6· Lauriane Rouan2,3· Jean-Noël Bacro4·Eric Gozé5,6Received : 1 November 2017 / Accepted : 17 October 2018
© Springer Science+Business Media, LLC, part of Springer Nature 2019

Abstract

The efficient global optimization method is popular for the global optimization of computer-intensive black-box functions. Extensions exist, either for the optimization of noisy functions,or for the conditional optimization of deterministic functions, i.e. the search for the values ofa subset of parameters that optimize the function conditionally to the values taken by anothersubset, which are fixed. A metaphor for conditional optimization is the search for a crest line.No method has yet been developed for the conditional optimization of noisy functions : thisis what we propose in this article. Testing this new method on test functions showed that,in the case of a high level of noise on the function, the PEQI criterion that we propose isbetter than the PEI criterion usually implemented in such a situation.

Keywords : Crest line · Gaussian process · Sampling criterion · Sequential design