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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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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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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DOI: https://doi.org/10.1007/s10898-018-0716-0