B. Efron and R. Tibshirani, An introduction to the bootstrap, of Monographs on Statistic and Applied Probability. Chapman & Hall, 1993.
DOI : 10.1007/978-1-4899-4541-9

S. Gelly, J. Mary, and O. Teytaud, On the ultimate convergence rates for isotropic algorithms and the best choices among various forms of isotropy, 10 th International Conference on Parallel Problem Solving from Nature, 2006.
URL : https://hal.archives-ouvertes.fr/inria-00112816

J. Grahl, P. A. Bosman, and F. Rothlauf, The correlation-triggered adaptive variance scaling IDEA, Proceedings of the 8th annual conference on Genetic and evolutionary computation , GECCO '06, pp.397-404, 2006.
DOI : 10.1145/1143997.1144071

D. Gross and C. L. Harris, Fundamentals of Queueing Theory, 1974.
DOI : 10.1002/9781118625651

G. R. Harik, F. G. Lobo, and D. E. Goldberg, The compact genetic algorithm, IEEE Transactions on Evolutionary Computation, vol.3, issue.4, p.287, 1999.
DOI : 10.1109/4235.797971

P. Larranaga and J. A. Lozano, Estimation of Distribution Algorithms. A New Tool for Evolutionary Computation, 2001.

P. L. Ecuyer and C. Lemieux, Recent Advances in Randomized Quasi-Monte Carlo Methods, pp.419-474, 2002.

J. Liu and H. Teng, Model Learning and Variance Control in Continuous EDAs Using PCA, 2008 3rd International Conference on Innovative Computing Information and Control, p.555, 2008.
DOI : 10.1109/ICICIC.2008.365

B. J. Morgan, Elements of simulation, 1984.
DOI : 10.1007/978-1-4899-3282-2

H. Mühlenbein and R. Höns, The estimation of Dim

H. Mühlenbein and T. Mahnig, Evolutionary computation and Wright's equation, Theoretical Computer Science, vol.287, issue.1, pp.145-165, 2002.
DOI : 10.1016/S0304-3975(02)00098-1

H. Niederreiter, Random Number Generation and Quasi-Monte Carlo Methods, 1992.
DOI : 10.1137/1.9781611970081

A. B. Owen, Quasi-Monte Carlo sampling, Monte Carlo Ray Tracing: Siggraph 2003 Course 44, pp.69-88, 2003.

P. Posík, Preventing premature convergence in a simple eda via global step size setting [21] A. Salmerón and S. Moral. Importance sampling in bayesian networks using antithetic variables, PPSN ECSQARU '01: Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, pp.549-558, 2001.

L. W. Schruben and B. H. Margolin, Pseudorandom Number Assignment in Statistically Designed Simulation and Distribution Sampling Experiments, Journal of the American Statistical Association, vol.4, issue.4, pp.504-525, 1978.
DOI : 10.1214/aoms/1177698603

J. L. Shapiro, Drift and Scaling in Estimation of Distribution Algorithms, Evolutionary Computation, vol.12, issue.4, 2005.
DOI : 10.1016/S0167-2789(96)00163-7

I. Sloan and H. Wo´zniakowskiwo´zniakowski, When Are Quasi-Monte Carlo Algorithms Efficient for High Dimensional Integrals?, Journal of Complexity, vol.14, issue.1, pp.1-33, 1998.
DOI : 10.1006/jcom.1997.0463

O. Teytaud, Conditionning, halting criteria and choosing lambda, EA07, Tours France Mathematics of Computing/G.1: NUMERICAL ANALYSIS, G.: Mathematics of Computing, 2007.
DOI : 10.1007/978-3-540-79305-2_17
URL : https://hal.archives-ouvertes.fr/inria-00173237

O. Teytaud, When Does Quasi-random Work?, PPSN, pp.325-336, 2008.
DOI : 10.1007/978-3-540-87700-4_33
URL : https://hal.archives-ouvertes.fr/inria-00287863

O. Teytaud and S. Gelly, DCMA, Proceedings of the 9th annual conference on Genetic and evolutionary computation , GECCO '07, pp.955-963, 2007.
DOI : 10.1145/1276958.1277150
URL : https://hal.archives-ouvertes.fr/inria-00173207

A. Van-der-vaart and J. Wellner, Weak Convergence and Empirical Processes, With Applications to Statistics, 1996.

S. Yakowitz, J. E. Krimmel, and F. Szidarovszky, Weighted Monte Carlo Integration, SIAM Journal on Numerical Analysis, vol.15, issue.6, pp.1289-1300, 1978.
DOI : 10.1137/0715088