J. Bergstra and Y. Bengio, Random search for hyper-parameter optimization, The Journal of Machine Learning Research, vol.13, pp.281-305, 2012.

P. Bhardwaj, B. Dasgupta, and K. Deb, Modeling Pareto-optimal set using B-spline basis functions, 2012.
DOI : 10.1080/0305215x.2013.812727

A. Bienvenüe and D. Rullì-ere, Iterative adjustment of survival functions by composed probability distortions. The Geneva Risk and Insurance Review, pp.156-179, 2012.

M. Binois, D. Ginsbourger, and O. Roustant, Quantifying uncertainty on Pareto fronts with Gaussian process conditional simulations, European Journal of Operational Research, vol.243, issue.2, p.2014
DOI : 10.1016/j.ejor.2014.07.032
URL : https://hal.archives-ouvertes.fr/hal-00904811

A. Bücher, H. Dette, and S. Volgushev, A test for Archimedeanity in bivariate copula models, Journal of Multivariate Analysis, vol.110, pp.121-132, 2012.
DOI : 10.1016/j.jmva.2012.01.026

Y. Chen and X. Zou, Runtime analysis of a multi-objective evolutionary algorithm for obtaining finite approximations of Pareto fronts, Information Sciences, vol.262, pp.62-77, 2014.
DOI : 10.1016/j.ins.2013.11.023

P. Dasgupta, P. P. Chakrabarti, and S. C. Desarkar, Multiobjective heuristic search: An introduction to intelligent search methods for multicriteria optimization, 1999.
DOI : 10.1007/978-3-322-86853-4

K. Deb, Multi-objective optimization using evolutionary algorithms, 2001.

K. Deb, Introduction to Evolutionary Multiobjective Optimization, Multiobjective Optimization, pp.59-96, 2008.
DOI : 10.1007/978-3-540-88908-3_3

P. Deheuvels, La fonction de dépendance empirique et ses propriétés, Acad. Roy. Belg. Bull. Cl. Sci, vol.65, issue.5, pp.274-292, 1979.

E. , D. Bernardino, and D. Rullì-ere, Distortions of multivariate distribution functions and associated level curves: Applications in multivariate risk theory, Insurance: Mathematics and Economics, vol.53, issue.1, pp.190-205, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00750873

D. Bernardino and D. Rullì-ere, On certain transformations of Archimedean copulas: Application to the non-parametric estimation of their generators, Dependence Modeling, vol.1, pp.1-36, 2013.
DOI : 10.2478/demo-2013-0001

D. S. Dimitrova, V. K. Kaishev, and S. I. Penev, GeD spline estimation of multivariate Archimedean copulas, Computational Statistics & Data Analysis, vol.52, issue.7, pp.3570-3582, 2008.
DOI : 10.1016/j.csda.2007.11.010
URL : http://openaccess.city.ac.uk/2311/1/179ARP.pdf

T. Duong, ks: Kernel smoothing, 2014.

P. Embrechts, F. Lindskog, and A. Mcneil, Modelling dependence with copulas and applications to risk management. Handbook of heavy tailed distributions in finance, pp.329-384, 2003.

A. Erdely, J. M. González-barrios, and M. M. , Hernández-Cedillo. Frank's condition for multivariate Archimedean copulas. Fuzzy Sets and Systems, p.2013

Y. Gao, L. Peng, F. Li, M. Liu, and X. Hu, Estimation of Distribution Algorithm with Multivariate <i>T</i>-Copulas for Multi-Objective Optimization, Intelligent Control and Automation, vol.04, issue.01, p.63, 2013.
DOI : 10.4236/ica.2013.41009

Y. Gao, L. Peng, F. Li, M. Liu, and W. Liu, Archimedean copula-based estimation of distribution algorithm for multi-objective optimisation, International Journal of Trust Management in Computing and Communications, vol.1, issue.3/4, pp.200-211, 2013.
DOI : 10.1504/IJTMCC.2013.056430

C. Genest and L. Rivest, Statistical Inference Procedures for Bivariate Archimedean Copulas, Journal of the American Statistical Association, vol.58, issue.423, pp.1034-1043, 1993.
DOI : 10.1214/aos/1176344685

C. Genest, J. Ne?lehová, and J. Ziegel, Inference in multivariate Archimedean copula models, TEST, vol.23, issue.2, pp.223-256, 2011.
DOI : 10.1007/s11749-011-0250-6

I. Giagkiozis and P. J. Fleming, Methods for multi-objective optimization: An analysis, Information Sciences, vol.293, issue.0, pp.338-350, 2015.
DOI : 10.1016/j.ins.2014.08.071

S. Girard, A. Guillou, and G. Stupfler, Estimating an endpoint with high-order moments, TEST, vol.47, issue.3, pp.697-729, 2012.
DOI : 10.1007/s11749-011-0277-8
URL : https://hal.archives-ouvertes.fr/inria-00596979

T. Goel, R. Vaidyanathan, R. T. Haftka, W. Shyy, N. V. Queipo et al., Response surface approximation of Pareto optimal front in multi-objective optimization, Computer Methods in Applied Mechanics and Engineering, vol.196, issue.4-6, pp.879-893, 2007.
DOI : 10.1016/j.cma.2006.07.010

C. Grosan and A. Abraham, Approximating Pareto frontier using a hybrid line search approach, Information Sciences, vol.180, issue.14, pp.2674-2695, 2010.
DOI : 10.1016/j.ins.2009.12.018
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.154.2400

P. Hall, On estimating the endpoint of a distribution. The Annals of Statistics, pp.556-568, 1982.

P. Hall and J. Z. Wang, Estimating the End-Point of a Probability Distribution Using Minimum-Distance Methods, Bernoulli, vol.5, issue.1, pp.177-189, 1999.
DOI : 10.2307/3318618

P. Hall, M. Nussbaum, and S. E. Stern, On the Estimation of a Support Curve of Indeterminate Sharpness, Journal of Multivariate Analysis, vol.62, issue.2, pp.204-232, 1997.
DOI : 10.1006/jmva.1997.1681

M. Hofert and M. Mächler, Nested Archimedean copulas meet R: The nacopula package, Journal of Statistical Software, vol.39, issue.9, pp.1-20, 2011.

M. Hofert, I. Kojadinovic, M. Maechler, and J. Yan, copula: Multivariate Dependence with Copulas, 2014. R package version 0, pp.999-1009

J. Hüsler, On applications of extreme value theory in optimization, Experimental Methods for the Analysis of Optimization Algorithms, pp.185-207, 2010.

P. Jaworski, On copulas and their diagonals, Information Sciences, vol.179, issue.17, pp.2863-2871, 2009.
DOI : 10.1016/j.ins.2008.09.006

G. Kim, M. J. Silvapulle, and P. Silvapulle, Comparison of semiparametric and parametric methods for estimating copulas, Computational Statistics & Data Analysis, vol.51, issue.6, pp.2836-2850, 2007.
DOI : 10.1016/j.csda.2006.10.009

I. Kojadinovic and J. Yan, Comparison of three semiparametric methods for estimating dependence parameters in copula models, Insurance: Mathematics and Economics, vol.47, issue.1, pp.52-63, 2010.
DOI : 10.1016/j.insmatheco.2010.03.008
URL : https://hal.archives-ouvertes.fr/hal-00868099

S. König, H. Kazianka, J. Pilz, and J. Temme, Estimation of nonstrict Archimedean copulas and its application to quantum networks, Applied Stochastic Models in Business and Industry, vol.37, issue.3, 2014.
DOI : 10.1002/asmb.2039

M. Laumanns and R. Zenklusen, Stochastic convergence of random search methods to fixed size Pareto front approximations, European Journal of Operational Research, vol.213, issue.2, pp.414-421, 2011.
DOI : 10.1016/j.ejor.2011.03.039

D. Li, L. Peng, and X. Xu, Bias reduction for endpoint estimation, Extremes, vol.2, issue.2, pp.393-412, 2011.
DOI : 10.1007/s10687-010-0118-2
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.154.9532

W. Loh, Estimating an endpoint of a distribution with resampling methods. The Annals of Statistics, pp.1543-1550, 1984.

A. J. Mcneil and J. Ne?lehová, Multivariate Archimedean copulas, d-monotone functions and l 1 ?norm symmetric distributions. The Annals of Statistics, pp.3059-3097, 2009.

G. Nappo and F. Spizzichino, Kendall distributions and level sets in bivariate exchangeable survival models, Information Sciences, vol.179, issue.17, pp.2878-2890, 2009.
DOI : 10.1016/j.ins.2009.02.007

R. B. Nelsen, An introduction to copulas, Lecture Notes in Statistics, vol.139, 1999.
DOI : 10.1007/978-1-4757-3076-0

M. Omelka, I. Gijbels, and N. Veraverbeke, Improved kernel estimation of copulas: weak convergence and goodness-of-fit testing. The Annals of Statistics, pp.3023-3058, 2009.

C. Poloni, A. Giurgevich, L. Onesti, and V. Pediroda, Hybridization of a multi-objective genetic algorithm, a neural network and a classical optimizer for a complex design problem in fluid dynamics, Computer Methods in Applied Mechanics and Engineering, vol.186, issue.2-4, pp.403-420, 2000.
DOI : 10.1016/S0045-7825(99)00394-1

W. Ponweiser, T. Wagner, D. Biermann, and M. Vincze, Multiobjective Optimization on a Limited Budget of Evaluations Using Model-Assisted $\mathcal{S}$ -Metric Selection, LNCS, vol.5199, pp.784-794, 2008.
DOI : 10.1007/978-3-540-87700-4_78

N. Queipo, S. Pintos, E. Nava, and A. Verde, Setting targets for surrogate-based optimization, Journal of Global Optimization, pp.1-19, 2010.

M. Sklar, Fonctions de répartitionrépartitionà n dimensions et leurs marges, Université Paris, vol.8, 1959.

I. Voutchkov and A. Keane, Multi-Objective Optimization Using Surrogates, Computational Intelligence in Optimization, pp.155-175, 2010.
DOI : 10.1007/978-3-642-12775-5_7

L. Wang and J. Zeng, Estimation of Distribution Algorithm based on copula theory, 2009 IEEE Congress on Evolutionary Computation, pp.139-162, 2010.
DOI : 10.1109/CEC.2009.4983063

A. R. Warburton, Quasiconcave vector maximization: Connectedness of the sets of Pareto-optimal and weak Pareto-optimal alternatives, Journal of Optimization Theory and Applications, vol.19, issue.4, pp.537-557, 1983.
DOI : 10.1007/BF00933970

J. Yan, Enjoy the joy of copulas: with a package copula, Journal of Statistical Software, vol.21, issue.4, pp.1-21, 2007.

A. Zhou, B. Qu, H. Li, S. Zhao, P. N. Suganthan et al., Multiobjective evolutionary algorithms: A survey of the state of the art, Swarm and Evolutionary Computation, vol.1, issue.1, pp.32-49, 2011.
DOI : 10.1016/j.swevo.2011.03.001

E. Zitzler, K. Deb, and L. Thiele, Comparison of Multiobjective Evolutionary Algorithms: Empirical Results, Evolutionary Computation, vol.8, issue.2, pp.173-195, 2000.
DOI : 10.1109/4235.797969
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.105.5926

E. Zitzler, J. Knowles, and L. Thiele, Quality Assessment of Pareto Set Approximations, Multiobjective Optimization, pp.373-404, 2008.
DOI : 10.1007/978-3-540-88908-3_14