  We consider the problem of global optimization of a function f from very
noisy evaluations. We adopt a Bayesian sequential approach: evaluation points
are chosen so as to reduce the uncertainty about the position of the global
optimum of f, as measured by the entropy of the corresponding random variable
(Informational Approach to Global Optimization, Villemonteix et al., 2009).
When evaluations are very noisy, the error coming from the estimation of the
entropy using conditional simulations becomes non negligible compared to its
variations on the input domain. We propose a solution to this problem by
choosing evaluation points as if several evaluations were going to be made at
these points. The method is applied to the optimization of a strategy for the
integration of renewable energies into an electrical distribution network.
