Optimization of simulation model output is one of the most important tasks in a simulation study of a complex system. Efficacy of an optimization approach is expressed in the accuracy of locating a global extremum, as well as in the number of investigated search points. The approach Machine ( ) Learning Optimization ML-Opt , presented in this article, explores functional dependencies between search points in order to reduce the number of evaluations. Functional relations between search points are determined by an inductive learning algorithm, which generates a classifier used as a control structure in the optimization process. The classifier approximates the structure of the unknown goal function given by a simulation model and affects the generation of new search points. A discussion of a numerical example concludes the paper.
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