Combinations of simulation and Evolutionary Algorithms in management science and economics

Abstract

Evolutionary Algorithms are robust search methods that mimic basic principles of evolution. We discuss different combinations of Evolutionary Algorithms and the versatile simulation method resulting in powerful tools not only for complex decision situations but explanatory models also. Realised and suggested applications from the domains of management and economics demonstrate the relevance of this approach. In a practical example three EA-variants produce better results than two conventional methods when optimising the decision variables of a stochastic inventory simulation. We show that EA are also more robust optimisers when only few simulations of each trial solution are performed. This characteristic may be used to reduce the generally higher CPU-requirements of population-based search methods like EA as opposed to point-based traditional optimisation techniques.