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Conditional optimization of a noisy function using a kriging metamodel

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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.

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Acknowledgements

We are very grateful to the West Africa Agricultural Productivity Program (WAAPP) that funded this research as part of a Ph.D. thesis grant.

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Authors and Affiliations

  1. Centre d’étude régional pour l’amélioration de l’adaptation à la sécheresse, Isra-Ceraas/Coraf, BP 3320, Thiès Escale, Thiès, Senegal

    Diariétou Sambakhé

  2. CIRAD, UMR AGAP, 34398, Montpellier, France

    Diariétou Sambakhé & Lauriane Rouan

  3. AGAP, Univ Montpellier, CIRAD, INRA, Montpellier SupAgro, Montpellier, France

    Diariétou Sambakhé & Lauriane Rouan

  4. IMAG, Univ Montpellier, CNRS, Montpellier, France

    Diariétou Sambakhé & Jean-Noël Bacro

  5. CIRAD, UPR AIDA, 34398, Montpellier, France

    Diariétou Sambakhé & Eric Gozé

  6. AIDA, Univ Montpellier, CIRAD, Montpellier, France

    Diariétou Sambakhé & Eric Gozé

Authors
  1. Diariétou Sambakhé
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  2. Lauriane Rouan
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  3. Jean-Noël Bacro
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  4. Eric Gozé
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Correspondence to Diariétou Sambakhé.

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Sambakhé, D., Rouan, L., Bacro, JN. et al. Conditional optimization of a noisy function using a kriging metamodel. J Glob Optim 73, 615–636 (2019). https://doi.org/10.1007/s10898-018-0716-0

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