E. Acar, M. Rais-rohani, P. Bradley, U. Fayyad, and R. C. , Ensemble of metamodels with optimized weight factors Survey of clustering data mining techniques Scaling EM (expectationmaximization ) clustering to large databases, Struct Multidisc Optim, vol.37, issue.3, 1998.

M. Buhmann, K. Burnham, and D. Anderson, Radial basis functions Multimodel inference: understanding AIC and BIC in model selection, Acta Numer Sociol Methods Res, vol.9, issue.332, pp.1-38261, 2001.

A. Dempster, N. Laird, and D. Rubin, Maximum likelihood from incomplete data via the EM algorithm, J R Stat Soc B (Methodological), vol.39, pp.1-38, 1977.

G. Dreyfus, Neural networks: methodology and applications Dual bases and discrete reproducing kernels: a unified framework for RBF and MLS approximation, Berlin Fasshauer GE Eng Anal Bound Elem, vol.29, issue.4, pp.313-325, 2005.

A. Forrester and A. Keane, Recent advances in surrogate-based optimization, Progress in Aerospace Sciences, vol.45, issue.1-3, pp.50-79, 2009.
DOI : 10.1016/j.paerosci.2008.11.001

J. Friedman, T. Hastie, and R. Tibshirani, The elements of statistical learning, 2001.

R. Haftka and Z. Gurdal, Elements of structural optimization Neural networks: a comprehensive foundation, Kluwer Haykin S, 1992.

M. Jordan, R. Jacobs, J. Kleijnen, S. Sanchez, T. Lucas et al., Hierarchical Mixtures of Experts and the EM Algorithm, Neural Computation, vol.26, issue.2, pp.181-214263, 1994.
DOI : 10.1214/aos/1176346060

R. Kohavi, A study of cross-validation and bootstrap for accuracy estimation and model selection, International joint conference on artificial intelligence, pp.1137-1145, 1995.

D. Levin, The approximation power of moving least-squares, Mathematics of Computation, vol.67, issue.224, pp.1517-1532, 1998.
DOI : 10.1090/S0025-5718-98-00974-0

B. Liu, R. Haftka, and L. Watson, Global-local structural optimization using response surfaces of local optimization margins, Structural and Multidisciplinary Optimization, vol.27, issue.5, pp.352-359, 2004.
DOI : 10.1007/s00158-004-0393-0

R. Meir and G. Ratsch, An Introduction to Boosting and Leveraging, Lect Notes Comput Sci, vol.2600, pp.118-183, 2003.
DOI : 10.1007/3-540-36434-X_4

A. Merval, M. Samuelides, S. Grihon, and . Ahs, Application of response surface methodology to stiffened panel optimizationASC structures, structural dynamics, and materials conference Response surface methodology, process and product optimization using designed experiments An as-short-as-possible introduction to the least squares, weighted least squares and moving least squares methods for scattered data approximation and interpolation, p.47, 2004.

R. Picard and R. Cook, Cross-Validation of Regression Models, Journal of the American Statistical Association, vol.9, issue.387, pp.575-583, 1984.
DOI : 10.1080/00401706.1977.10489581

E. Sanchez, S. Pintos, N. Queipo, T. Simpson, V. Toropov et al., Toward an optimal ensemble of kernel-based approximations with engineering applications Design and analysis of computer experiments in multidisciplinary design optimization: a review of how far we have come or not A tutorial on support vector regression Kriging interpolation in simulation: a survey, Proceedings of the 12th AIAA/ISSMO multidisciplinary analysis and optimization conference199?222 van Beers WCM Proceedings of the 36th conference on winter simulation. Winter simulation conference, pp.247-261, 2004.

F. Viana, R. Haftka, and V. Steffen, Multiple surrogates: how cross-validation errors can help us to obtain the best predictor, Structural and Multidisciplinary Optimization, vol.47, issue.4, pp.439-457, 2009.
DOI : 10.1007/s00158-008-0338-0

G. Wang and S. Shan, Review of Metamodeling Techniques in Support of Engineering Design Optimization, Journal of Mechanical Design, vol.26, issue.28, p.370, 2007.
DOI : 10.1023/A:1023283917997

C. Wu, On the Convergence Properties of the EM Algorithm, The Annals of Statistics, vol.11, issue.1, pp.95-103, 1983.
DOI : 10.1214/aos/1176346060

Y. Yang, Regression with multiple candidate models: selecting or mixing?, Stat Sin, vol.13, issue.3, pp.783-810, 2003.

L. Zerpa, N. Queipo, S. Pintos, and J. Salager, An optimization methodology of alkaline???surfactant???polymer flooding processes using field scale numerical simulation and multiple surrogates, Journal of Petroleum Science and Engineering, vol.47, issue.3-4, pp.197-208, 2005.
DOI : 10.1016/j.petrol.2005.03.002