D. R. Jones, M. Schonlau, and J. William, Efficient global optimization of expensive black-box functions, Journal of Global Optimization, vol.13, issue.4, pp.455-492, 1998.
DOI : 10.1023/A:1008306431147

T. J. Santner, B. J. Williams, and W. Notz, The Design and Analysis of Computer Experiments, 2003.
DOI : 10.1007/978-1-4757-3799-8

J. Mockus, Bayesian Approach to Global Optimization. Theory and Applications, 1989.

O. Roustant, D. Ginsbourger, and Y. Deville, DiceKriging, DiceOptim: Two R packages for the analysis of computer experiments by Kriging-Based Metamodelling and Optimization, Journal of Statistical Software, vol.51, issue.1, pp.1-55, 2012.
URL : https://hal.archives-ouvertes.fr/emse-00741762

J. Mockus, V. Tiesis, and A. Zilinskas, The application of Bayesian methods for seeking the extremum, Towards Global Optimization, vol.2, pp.117-129, 1978.

M. Schonlau, Computer Experiments and global optimization, 1997.

D. Ginsbourger, Métamodèles multiples pour l'approximation et l'optimisation de fonctions numériques multivariables, 2009.

D. Ginsbourger, R. Le-riche, L. , and C. , Kriging Is Well-Suited to Parallelize Optimization, of Adaptation Learning and Optimization, pp.131-162, 2010.
DOI : 10.1007/978-3-642-10701-6_6

URL : https://hal.archives-ouvertes.fr/emse-00436126

J. Janusevskis, R. Le-riche, and D. Ginsbourger, Parallel expected improvements for global optimization: summary, bounds and speed-up, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00613971

J. Janusevskis, R. Le-riche, D. Ginsbourger, and R. Girdziusas, Expected Improvements for the Asynchronous Parallel Global Optimization of Expensive Functions: Potentials and Challenges, LION 6 Conference (Learning and Intelligent OptimizatioN ), 2012.
DOI : 10.1007/978-3-642-34413-8_37

URL : https://hal.archives-ouvertes.fr/emse-00686504

P. I. Frazier, Parallel global optimization using an improved multi-points expected improvement criterion, 2012.

J. P. Chiì-es and P. Delfiner, Geostatistics: Modeling Spatial Uncertainty, 1999.

G. Tallis, The moment generating function of the truncated multi-normal distribution, J. Roy. Statist. Soc. Ser. B, vol.23, issue.1, pp.223-229, 1961.

D. Veiga, S. Marrel, and A. , Gaussian process modeling with inequality constraints, Annales de la facult?? des sciences de Toulouse Math??matiques, vol.21, issue.3, pp.529-555, 2012.
DOI : 10.5802/afst.1344

A. Genz, Numerical Computation of Multivariate Normal Probabilities, Journal of Computational and Graphical Statistics, vol.1, issue.2, pp.141-149, 1992.
DOI : 10.1007/978-1-4613-9655-0

A. Genz and F. Bretz, Computation of Multivariate Normal and t Probabilities, 2009.
DOI : 10.1007/978-3-642-01689-9

A. Azzalini, mnormt: The multivariate normal and t distributions. (2012) R package version 1, pp.4-5

S. Finck, N. Hansen, R. Ros, and A. Auger, Real-parameter black-box optimization bencharking 2009: Presentation of the noiseless functions, 2009.

N. Hansen, S. Finck, R. Ros, and A. Auger, Real-parameter black-box optimization bencharking 2009: Noiseless functions definitions, p.2009, 2010.

W. Mebane and J. Sekhon, Genetic optimization using derivatives: The rgenoud package for r, Journal of Statistical Software, vol.42, issue.11, pp.1-26, 2011.

N. Cressie, A. Davis, and J. Leroy-folks, The moment-generating function and negative integer moments, The American Statistician, vol.35, issue.3, pp.148-150, 1981.