Conditional optimization of a noisy function using a kriging metamodel

Diariétou Sambakhé (), Lauriane Rouan, Jean-Noël Bacro and Eric Gozé
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Diariétou Sambakhé: Centre d’étude régional pour l’amélioration de l’adaptation à la sécheresse
Lauriane Rouan: CIRAD, UMR AGAP
Jean-Noël Bacro: IMAG, Univ Montpellier, CNRS
Eric Gozé: CIRAD, UPR AIDA

Journal of Global Optimization, 2019, vol. 73, issue 3, No 8, 615-636

Abstract: 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 of a subset of parameters that optimize the function conditionally to the values taken by another subset, 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: this is 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 is better than the PEI criterion usually implemented in such a situation.

Keywords: Crest line; Gaussian process; Sampling criterion; Sequential design; Noisy function (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10898-018-0716-0

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