An algorithm for finding global optima using statistical prediction is presented. Assuming a random function model, lower confidence bounds on predicted values are used for sequential selection of evaluation points and as a convergence criterion. Comparison with published results for several test functions indicates that the procedure is very efficient in finding the global optimum of a multimodal function, and in terminating with relatively few evaluations.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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Cox et al. (2003) studied this question.
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