Global optimization of expensive black box problems with a known lower bound

In this paper we propose an algorithm for the global optimization of computa- tionally expensive black–box functions. For this class of problems, no information, like e.g. the gradient, can be obtained and function evaluation is highly expensive. In many appli- cations, however, a lower bound on the objective function is known; in this situation we derive a modified version of the algorithm introduced in Gutmann (J Glob Optim 19:201– 227, 2001). Using this information produces a significant improvement in the quality of the resulting method, with only a small increase in the computational cost. Extensive computa- tional results are provided which support this statement.