TY  - CHAP
N2  - Research on metamodel-based optimization has received considerably increasing interest in recent years, and has found successful applications in solving computationally expensive problems. The joint use of computer simulation experiments and metamodels introduces a source of uncertainty that we refer to as metamodel variability. To analyze and quantify this variability, we apply bootstrapping to residuals derived as prediction errors computed from cross-validation. The proposed method can be used with different types of metamodels, especially when limited knowledge on parameters? distribution is available or when a limited computational budget is allowed. Our preliminary experiments based on the robust version of
the EOQ model show encouraging results.
TI  - Metamodel variability analysis combining bootstrapping and validation techniques
ID  - eprints1444
SN  - 978-1-4673-4780-8
N1  - Article No.: 334
AV  - public
T2  - WSC '12 Proceedings of the Winter Simulation Conference 
PB  - ACM
Y1  - 2012/12//
UR  - http://dl.acm.org/citation.cfm?id=2430206
A1  - Dellino, Gabriella
A1  - Meloni, Carlo
ER  -