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Hybrid search for optimum in a small implicitly defined region

We consider optimization problems with a small implicitly defined feasible region, and with an objective function corrupted by irregularities, e.g. small noise added to the function values. Known mathematical programming methods with high convergence rate can not be applied to such problems. A hybrid technique is developed combining random search for the feasible region of a considered problem, and evolutionary search for the minimum over the found region. The solution results of two test problems and of a difficult real world problem are presented.