E. I. Atanassov, On the discrepancy of the Halton sequences, Mathematica Balkanica, vol.18, pp.15-32, 2004.

E. I. Atanassov and M. K. Durchova, Generating and Testing the Modified Halton Sequences, Proceedings of the Conference on Numerical Methods and Applications (NMA'02), pp.91-98, 2003.
DOI : 10.1007/3-540-36487-0_9

A. Auger, M. Jebalia, and O. Teytaud, Algorithms (X, sigma, eta): Quasi-random Mutations for Evolution Strategies, Proceedings of the Artificial Evolution Conference (EA'05), pp.296-307, 2006.
DOI : 10.1007/11740698_26
URL : https://hal.archives-ouvertes.fr/inria-00000544

J. Bates, J. Sienz, and V. V. Toropov, Formulation of the Optimal Latin Hypercube Design of Experiments Using a Permutation Genetic Algorithm, 45th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics & Materials Conference, 2004.
DOI : 10.2514/6.2004-2011

E. Braaten and G. Weller, An improved low-discrepancy sequence for multidimensional quasi-Monte Carlo integration, Journal of Computational Physics, vol.33, issue.2, pp.249-258, 1979.
DOI : 10.1016/0021-9991(79)90019-6

R. E. Caflisch, W. J. Morokoff, and A. B. Owen, Valuation of mortgage-backed securities using Brownian bridges to reduce effective dimension, The Journal of Computational Finance, vol.1, issue.1, pp.27-46, 1997.
DOI : 10.21314/JCF.1997.005

C. Cervellera and M. Muselli, Deterministic Design for Neural Network Learning: An Approach Based on Discrepancy, IEEE Transactions on Neural Networks, vol.15, issue.3, pp.533-544, 2004.
DOI : 10.1109/TNN.2004.824413

M. Chi, T. T. Mascagni, and . Warnock, On the optimal Halton sequence, Mathematics and Computers in Simulation, vol.70, issue.1, pp.9-21, 2005.
DOI : 10.1016/j.matcom.2005.03.004

V. A. Cicirello and S. F. Smith, Modeling GA performance for control parameter optimization, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pp.235-242, 2000.

T. M. Cioppa, Efficient Nearly Orthogonal and Space-Filling Experimental for High- Dimensional Complex Models, Naval Postgraduate School, 2002.

M. Cioppa and T. W. Lucas, Efficient Nearly Orthogonal and Space-Filling Latin Hypercubes, Technometrics, vol.49, issue.1, pp.45-55, 2007.
DOI : 10.1198/004017006000000453
URL : http://hdl.handle.net/10945/35341

K. Deb, Multi-Objective Optimization Using Evolutionary Algorithms, 2000.

K. Deb, A. Pratab, S. Agarwal, and T. Meyarivan, A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimization: NSGA-II, IEEE Trans. Evolut. Comput, vol.6, issue.2, pp.849-858, 2002.
DOI : 10.1007/3-540-45356-3_83

K. T. Fang, D. K. Lin, P. Winker, and Y. Zhang, Uniform Design: Theory and Application, Technometrics, vol.34, issue.3, pp.237-248, 2000.
DOI : 10.1016/0167-9473(94)00041-G

H. Faure, Discrépance de suites associéesassociéesà un système de numération (en dimension s) Acta Arithmetica, pp.337-351, 1982.
DOI : 10.1515/9783110852790.207

H. Faure, Selection Criteria for (Random) Generation of Digital (0,s)-Sequences
DOI : 10.1007/3-540-31186-6_8

H. Faure and C. Lemieux, Generalized Halton sequences in 2008, ACM Transactions on Modeling and Computer Simulation, vol.19, issue.4, pp.1-43, 2009.
DOI : 10.1145/1596519.1596520

A. Florian, An efficient sampling scheme: Updated Latin Hypercube Sampling, Probabilistic Engineering Mechanics, vol.7, issue.2, pp.123-130, 1992.
DOI : 10.1016/0266-8920(92)90015-A

P. Glasserman, Monte Carlo Methods in Financial Enginerring, 2004.

J. H. Halton, On the efficiency of certain quasi-random sequences of points in evaluating multi-dimensional integrals, Numerische Mathematik, vol.38, issue.1, pp.84-90, 1960.
DOI : 10.1007/BF01386213

J. M. Hammersley, MONTE CARLO METHODS FOR SOLVING MULTIVARIABLE PROBLEMS, Annals of the New York Academy of Sciences, vol.28, issue.3, pp.844-874, 1960.
DOI : 10.1111/j.1749-6632.1960.tb42846.x

F. J. Hickernell, A generalized discrepancy and quadrature error bound, Mathematics of Computation of the American Mathematical Society, vol.67, issue.221
DOI : 10.1090/S0025-5718-98-00894-1
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.100.2225

J. H. Holland, Adaptation in Natural and Artificial Systems, 1975.

J. and F. Y. Kuo, Remark on algorithm 659: Implementing Sobol's quasirandom sequence generator, ACM Trans. Math. Softw, vol.29, pp.49-57, 2003.

M. E. Johnson, L. M. Moore, and D. Ylvisaker, Minimax and maximin distance designs, Journal of Statistical Planning and Inference, vol.26, issue.2, pp.131-148, 1990.
DOI : 10.1016/0378-3758(90)90122-B

L. Kocis and W. J. Whiten, Computational investigations of low-discrepancy sequences, ACM Transactions on Mathematical Software, vol.23, issue.2, pp.266-294, 1997.
DOI : 10.1145/264029.264064

J. R. Koza, Evolving a computer program to generate random numbers using the genetic programming paradigm, Proceedings of the 4th International Conference on Genetic Algorithm, pp.37-44, 1991.

P. L. Ecuyer, Quasi-Monte Carlo methods with applications in finance, Finance Stochast, vol.13, pp.307-349, 2009.

P. L. Ecuyer and C. Lemieux, Variance Reduction via Lattice Rules, Management Science, vol.46, issue.9, pp.1214-1235, 2000.
DOI : 10.1287/mnsc.46.9.1214.12231

P. L. Ecuyer and C. Lemieux, Recent advances in randomized quasi-Monte Carlo methods, Proceedings of the Conference on Modeling Uncertainty: An Examination of Stochastic Theory , Methods, and Applications, pp.479-474, 2002.

C. Lemieux, Monte Carlo and Quasi Monte Carlo Sampling, 2009.

M. Liefvendahl and R. Stocki, A study on algorithms for optimization of Latin hypercubes, Journal of Statistical Planning and Inference, vol.136, issue.9, pp.3231-3247, 2006.
DOI : 10.1016/j.jspi.2005.01.007

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, issue.2, pp.239-245, 1979.

W. J. Morokoff and R. E. Caflisch, Quasi-Random Sequences and Their Discrepancies, SIAM Journal on Scientific Computing, vol.15, issue.6, pp.1251-1279, 1994.
DOI : 10.1137/0915077
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.298.5482

H. Niederreiter, Quasi-Monte Carlo methods and pseudo-random numbers, Bulletin of the American Mathematical Society, vol.84, issue.6, pp.957-1041, 1978.
DOI : 10.1090/S0002-9904-1978-14532-7

H. Niederreiter, Point sets and sequences with small discrepancy, Monatshefte f??r Mathematik, vol.7, issue.2, pp.273-337, 1987.
DOI : 10.1007/BF01294651

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

A. B. Owen, The dimension distribution and quadrature test functions, Statistica Sinica, vol.13, pp.1-17, 2003.

A. Papageorgiou and J. F. Traub, Faster Evaluation of Multidimensional Integrals, Computers in Physics, vol.11, issue.6, pp.574-579, 1997.
DOI : 10.1063/1.168616

M. Sipper and M. Tomassini, Co-evolving parallel random number generators, Proceedings of the Conference on Parallel Problem Solving from Nature (PPSNIV), volume 1141 of Lecture Notes in Computer Science, pp.950-959, 1996.
DOI : 10.1007/3-540-61723-X_1058
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.21.1044

I. H. 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

M. Sobol, On the distribution of points in a cube and the approximate evaluation of integrals, USSR Computational Mathematics and Mathematical Physics, vol.7, issue.4, pp.86-112, 1967.
DOI : 10.1016/0041-5553(67)90144-9

I. M. and D. I. Asostsky, One more experiment on estimating highdimensional integrals by quasi-Monte Carlo methods, Math. Comput. Simul, vol.62, pp.3-6255, 2003.

B. Tuffin, A new permutation choice in Halton sequences, Proceedings of the Conference on Monte Carlo and Quasi-Monte Carlo Methods, pp.427-435, 1998.
DOI : 10.1007/978-1-4612-1690-2_30

B. Vandewoestyne and R. Cools, Good permutations for deterministic scrambled Halton sequences in terms of L 2 -discrepancy, Comput. Appl. Math, vol.189, issue.12, p.341361, 2006.

X. Wang and K. T. Fang, The effective dimension and quasi-Monte Carlo integration, Journal of Complexity, vol.19, issue.2, pp.101-124, 2003.
DOI : 10.1016/S0885-064X(03)00003-7

X. Wang and F. J. Hickernell, Randomized Halton sequences, Mathematical and Computer Modelling, vol.32, issue.7-8, pp.887-899, 2000.
DOI : 10.1016/S0895-7177(00)00178-3
URL : http://doi.org/10.1016/s0895-7177(00)00178-3

X. Wang and I. Sloan, Low discrepancy sequences in high dimensions: How well are their projections distributed?, Journal of Computational and Applied Mathematics, vol.213, issue.2, pp.366-386, 2008.
DOI : 10.1016/j.cam.2007.01.005

T. T. Warnock, Computational Investigations of Low-Discrepancy Point Sets II, Applications of Number Theory to Numerical Analysis, pp.319-343, 1972.
DOI : 10.1007/978-1-4612-2552-2_23