Optimal selection probability in the two-stage nested partitions method for simulation-based optimization

We investigate a novel algorithm for simulation based optimization where the number of alternatives is finite but very large. This algorithm draws on recent work in adaptive random search and from ranking-and-selection. We show how the ranking-and-selection approach can significantly improve performance of the random search and demonstrate the importance of the probability of correct selection.