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

Created by W.Langdon from gp-bibliography.bib Revision:1.8178

@Article{Can2012424,

• author = "Birkan Can and Cathal Heavey",
• title = "A comparison of genetic programming and artificial neural networks in metamodeling of discrete-event simulation models",
• journal = "Computer \& Operations Research",
• volume = "39",
• number = "2",
• pages = "424--436",
• year = "2012",
• month = feb,
• ISSN = "0305-0548",
• DOI = "doi:10.1016/j.cor.2011.05.004",
• URL = "http://www.sciencedirect.com/science/article/pii/S0305054811001286",
• keywords = "genetic algorithms, genetic programming, Simulation metamodel, Symbolic regression, ANN, Neural networks, Design of experiments, Decision support tool",
• ISSN = "0305-0548",
• size = "13 pages",
• abstract = "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 metamodelling 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 generalisation 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 metamodelling of DES models.",
• notes = "p432 'The results show that across all three systems GP provided greater extrapolation capability'",
• bibdate = "2011-06-20",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/cor/cor39.html#CanH12",

}

Genetic Programming entries for Birkan Can Cathal Heavey

Citations