A comparison of genetic programming and artificial neural networks in metamodeling of discrete-event simulation models

Publication year: 2012
Source: Computers & Operations Research, Volume 39, Issue 2, February 2012, Pages 424-436

Birkan, Can , Cathal, Heavey

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….