Enhancing Evolutionary Algorithms with Statistical Selection Procedures for Simulation Optimization

In this paper, we present an evolution strategy for the optimization of simulation models. Our approach incorporates statistical selection procedures that efficiently select the best individual, where best is defined by the maximum or minimum expected simulation response. We use statistical procedures for the survivor selection during the evolutionary process and for selecting the best individual from a set of candidate best individuals, a so-called elite population, at the end of the evolutionary process. Furthermore, we propose a heuristic selection procedure that reduces a random-size subset, containing the best individual, to at most a predefined size. By means of a stochastic sphere function and a simulation model of a production line, we show that this procedure performs better in terms of number of model evaluations and solution quality than other state-of-the-art statistical selection procedures.