Inductive learning for optimization of simulation model output

In this article we present the optimization approach, `ML-Opt´, which approximates the structure of an unknown goal function by analyzing functional dependency between search points. The functional dependency is determined by an inductive learning algorithm, which generates a classifier used as a control structure in the optimization process. A numerical example and discussions are presented