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Conditional optimization of a noisy function using a kriging metamodel

Author

Listed:
  • Diariétou Sambakhé

    (Centre d’étude régional pour l’amélioration de l’adaptation à la sécheresse
    CIRAD, UMR AGAP
    AGAP, Univ Montpellier, CIRAD, INRA, Montpellier SupAgro
    IMAG, Univ Montpellier, CNRS)

  • Lauriane Rouan

    (CIRAD, UMR AGAP
    AGAP, Univ Montpellier, CIRAD, INRA, Montpellier SupAgro)

  • Jean-Noël Bacro

    (IMAG, Univ Montpellier, CNRS)

  • Eric Gozé

    (CIRAD, UPR AIDA
    AIDA, Univ Montpellier, CIRAD)

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.

Suggested Citation

  • Diariétou Sambakhé & Lauriane Rouan & Jean-Noël Bacro & Eric Gozé, 2019. "Conditional optimization of a noisy function using a kriging metamodel," Journal of Global Optimization, Springer, vol. 73(3), pages 615-636, March.
    • Handle: RePEc:spr:jglopt:v:73:y:2019:i:3:d:10.1007_s10898-018-0716-0
    DOI: 10.1007/s10898-018-0716-0
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    References listed on IDEAS

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    1. Roustant, Olivier & Ginsbourger, David & Deville, Yves, 2012. "DiceKriging, DiceOptim: Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 51(i01).
    2. D. Huang & T. Allen & W. Notz & N. Zeng, 2006. "Global Optimization of Stochastic Black-Box Systems via Sequential Kriging Meta-Models," Journal of Global Optimization, Springer, vol. 34(3), pages 441-466, March.
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    4. Picheny, Victor & Ginsbourger, David, 2014. "Noisy kriging-based optimization methods: A unified implementation within the DiceOptim package," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 1035-1053.
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