A comparison of genetic programming and artificial neural networks in metamodeling of discrete-event simulation models
Genetic programming (GP) and artificial neural networks (ANNs) can be used in the development of surrogate models of complex systems. The purpose of this paper is to provide a comparative analysis of GP and ANNs for metamodeling of discrete-event simulation (DES) models. Three stochastic industrial systems are empirically studied: an automated material handling system (AMHS) in semiconductor manufacturing, an (s,S) inventory model and a serial production line. The results of the study show that GP provides greater accuracy in validation tests, demonstrating a better generalization capability than ANN. However, GP when compared to ANN requires more computation in metamodel development. Even given this increased computational requirement, the results presented indicate that GP is very competitive in metamodeling of DES models.
genetic programming neural networks simulation metamodel symbolic regression design of experiments decision support tool