A Non-myopic Utility Function for Statistical Global Optimization Algorithms

Abstract

The high cost of providing “worst-case” solutions to global optimization problems has motivated the development of ”average-case“ algorithms that rely on a statistical model of the objective function. The critical role of the statistical model is to guide the search for the optimum. The standard approach is to define a utility function u(x) that in a certain sense reflects the benefit of evaluating the function at x. A proper utility function needs to strike a balance between the immediate benefit of evaluating the function at x – a myopic consideration; and the overall effect of this choice on the performance of the algorithm – a global criterion. The utility functions currently used in this context are heuristically modified versions of some myopic utility functions. We propose using a new utility function that is provably a globally optimal utility function in a non-adaptive context (where the model of the function values remains unchanged). In the adaptive context, this utility function is not necessarily optimal, however, given its global nature, we expect that its use will lead to the improved performance of statistical global optimization algorithms. To illustrate the approach, and to test the above assertion, we apply this utility function to an existing adaptive multi-dimensional statistical global optimization algorithm and provide experimental comparisons with the original algorithm.