M. Abramson, C. Audet, J. Chrissis, and J. Walston, Mesh adaptive direct search algorithms for mixed variable optimization, Optim Lett, vol.3, issue.1, pp.35-47, 2009.

R. J. Adler, The geometry of random fields, 1981.

C. Audet and J. E. Dennis, Pattern search algorithms for mixed variable programming, SIAM J Optim, vol.11, issue.3, pp.573-594, 2001.

C. Audet, J. E. Dennis, and J. , Mesh adaptive direct search algorithms for constrained optimization, SIAM J Optim, vol.17, issue.1, pp.188-217, 2006.

R. H. Byrd, P. Lu, J. Nocedal, and C. Zhu, A limited memory algorithm for bound constrained optimization, SIAM J Sci Comput, vol.16, issue.5, pp.1190-1208, 1995.

, COCO. 2017. Black-box optimization benchmarking

F. Comola, C. Janna, A. Lovison, M. Minini, A. Tamburini et al., Efficient global optimization of reservoir geomechanical parameters based on synthetic aperture radar-derived ground displacements, Geophysics, vol.81, issue.3, pp.23-33, 2016.

A. Costa and G. Nannicini, RBFOpt: an open-source library for black-box optimization with costly function evaluations, Math Prog Comp, vol.10, issue.4, pp.597-629, 2018.

R. B. Gramacy and M. Taddy, Categorical inputs, sensitivity analysis, optimization and importance tempering with tgp version 2, an r package for treed gaussian process models, J Stat Soft, vol.33, issue.6, pp.1-48, 2010.

H. M. Gutmann, A radial basis function method for global optimization, J Global Optim, vol.19, issue.3, pp.201-227, 2001.

K. Hamza and M. Shalaby, A framework for parallelized efficient global optimization with application to vehicle crashworthiness optimization, Eng Optim, vol.46, issue.9, pp.1200-1221, 2014.

G. Han, T. Santner, W. Notz, and D. Bartel, Prediction for computer experiments having quantitative and qualitative input variables, Technometrics, vol.51, issue.3, pp.278-288, 2009.

C. Helbert, D. Dupuy, and L. Carraro, Assessment of uncertainty in computer experiments: From universal kriging to bayesian kriging, Appl Stochastic Models Bus Ind, vol.25, issue.2, pp.99-113, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00407651

W. Hock and K. Schittkowski, Test examples for nonlinear programming codes, Lecture notes in economics and mathematical systems, vol.187, 1981.

D. R. Jones, M. Schonlau, and W. Welch, Efficient global optimization of expensive black-box functions, Journal of Global Optimization, vol.13, issue.4, pp.455-492, 1998.

M. Kanazaki, T. Matsuno, K. Maeda, and H. Kawazoe, Efficient global optimization applied to wind tunnel evaluation-based optimization for improvement of flow control by plasma actuators, Eng Optim, vol.47, issue.9, pp.1226-1242, 2015.

G. Liuzzi, S. Lucidi, and F. Rinaldi, Derivative-free methods for bound constrained mixed-integer optimization, Comput Optim Appl, vol.53, issue.2, pp.505-526, 2012.

L. Luk-san and J. Vl, Test problems for nonsmooth unconstrained and linearly constrained optimization, 2000.

M. D. Mckay, R. J. Beckman, and W. J. Conover, A comparison of three methods for selecting values of input variables in the analysis of output from a computer code, Technometrics, vol.21, pp.239-245, 1979.

J. J. Mor-e and S. M. Wild, Benchmarking derivative-free optimization algorithms, SIAM J Optim, vol.20, issue.1, pp.172-191, 2009.

J. Muller, C. A. Shoemaker, P. , and R. , SO-MI: A surrogate model algorithm for computationally expensive nonlinear mixed-integer black-box global optimization problems, Comput Oper Res, vol.40, issue.5, pp.1383-1400, 2013.

J. Muller, MISO: mixed-integer surrogate optimization framework, Optim Eng, vol.17, issue.1, pp.177-203, 2016.

J. Pelamatti, L. Brevault, M. Balesdent, E. G. Talbi, and T. Guerin, Efficient global optimimzation of constrained mixed variable problems, J Glob Optim, vol.73, pp.583-613, 2018.

J. Pinheiro and D. Bates, Unconstrained parametrizations for variance-covariance matrices, Stat Comput, vol.6, issue.3, pp.289-296, 1996.

J. Pinheiro and D. Bates, Mixed-effects models in s and s-plus, Statistics and Computing Springer, 2009.

K. Potdar, S. T. Pardawala, and D. C. Pai, A comparative study of categorical variable encoding techniques for neural network classifiers, Int J Comput Appl, vol.175, issue.4, pp.7-9, 2017.

P. Qian, H. Wu, and J. Wu, Gaussian process models for computer experiments with qualitative and quantitative factors, Annals of Statistics. Technometrics, vol.50, issue.3, pp.393-399, 2008.

C. E. Rasmussen, Gaussian processes for machine learning, 2006.

O. Roustant, D. Ginsbourger, and Y. Deville, Dicekriging, diceoptim: Two r packages for the analysis of computer experiments by kriging-based metamodeling and optimization, J Stat Soft, vol.51, issue.1, pp.1-55, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00495766

O. Roustant, E. Padonou, Y. Deville, A. Cl-ement, G. Perrin et al., Group kernels for Gaussian process metamodels with categorical inputs, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01702607

J. Sacks, W. Welch, T. Mitchell, and H. Wynn, Design and analysis of computer experiments, Statist Sci, vol.4, issue.4, pp.409-435, 1989.

T. Santner, B. Williams, and W. Notz, The design and analysis of computer experiments, 2003.

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

C. Waterloo,

L. Swiler, P. Hough, P. Qian, X. Xu, C. Storlie et al., Surrogate models for mixed discrete-continuous variables, pp.181-202, 2014.

M. Taddy, H. Lee, G. A. Gray, and J. D. Griffin, Bayesian guided pattern search for robust local optimization, Technometrics, vol.51, issue.4, pp.389-401, 2009.

Y. Zhang and W. Notz, Computer experiments with qualitative and quantitative variables: A review and reexamination, Qual Eng, vol.27, issue.1, pp.2-13, 2015.

Q. Zhou, P. Qian, and S. Zhou, A simple approach to emulation for computer models with qualitative and quantitative factors, Technometrics, vol.53, issue.3, pp.266-273, 2011.