N. Beume, S-Metric Calculation by Considering Dominated Hypervolume as Klee's Measure Problem, Evolutionary Computation, vol.17, issue.4, pp.477-492, 2009.
DOI : 10.1109/TEVC.2003.810758

N. Beume, B. Naujoks, and M. Emmerich, SMS-EMOA: Multiobjective selection based on dominated hypervolume, European Journal of Operational Research, vol.181, issue.3, pp.1653-1669, 2007.
DOI : 10.1016/j.ejor.2006.08.008

N. Beume and G. Rudolph, Faster S-metric calculation by considering dominated hypervolume as Klee's measure problem, IASTED International Conference on Computational Intelligence, pp.231-236, 2006.
DOI : 10.1162/evco.2009.17.4.17402

S. Bleuler, M. Laumanns, L. Thiele, and E. Zitzler, PISA ??? A Platform and Programming Language Independent Interface for Search Algorithms, Evolutionary Multi-Criterion Optimization, pp.494-508, 2003.
DOI : 10.1007/3-540-36970-8_35

K. Bringmann and T. Friedrich, Approximating the Least Hypervolume Contributor: NP-Hard in General, But Fast in Practice, Lecture Notes in Computer Science, vol.7, pp.6-20, 2009.
DOI : 10.1109/TEVC.2003.810758

K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, vol.6, issue.2, pp.182-197, 2002.
DOI : 10.1109/4235.996017

K. Deb, L. Thiele, M. Laumanns, and E. Zitzler, Scalable multi-objective optimization test problems, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600), pp.825-830, 2002.
DOI : 10.1109/CEC.2002.1007032

J. Dem?ar, Statistical comparisons of classifiers over multiple data sets, Journal of Machine Learning Research, vol.7, pp.1-30, 2006.

S. Garc?a and F. Herrera, An extension on " statistical comparisons of classifiers over multiple data sets " for all pairwise comparisons, Journal of Machine Learning Research, vol.9, pp.2677-2694, 2008.

S. Garc?a, D. Molina, M. Lozano, and F. Herrera, A study on the use of non-parametric tests for analyzing the evolutionary algorithms??? behaviour: a??case study on??the??CEC???2005 Special Session on??Real Parameter Optimization, Journal of Heuristics, vol.48, issue.1, pp.617-644, 2009.
DOI : 10.1007/s10732-008-9080-4

N. Hansen, S. D. Müller, and P. Koumoutsakos, Reducing the Time Complexity of the Derandomized Evolution Strategy with Covariance Matrix Adaptation (CMA-ES), Evolutionary Computation, vol.11, issue.1, pp.1-18, 2003.
DOI : 10.1162/106365601750190398

N. Hansen and A. Ostermeier, Completely Derandomized Self-Adaptation in Evolution Strategies, Evolutionary Computation, vol.9, issue.2, pp.159-195, 2001.
DOI : 10.1016/0004-3702(95)00124-7

C. Igel, N. Hansen, and S. Roth, Covariance Matrix Adaptation for Multi-objective Optimization, Evolutionary Computation, vol.15, issue.1, pp.1-28, 2006.
DOI : 10.1109/TEVC.2003.810758

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.469.4052

C. Igel, T. Suttorp, and N. Hansen, A computational efficient covariance matrix update and a (1+1)-CMA for evolution strategies, Proceedings of the 8th annual conference on Genetic and evolutionary computation , GECCO '06, pp.453-460, 2006.
DOI : 10.1145/1143997.1144082

C. Igel, T. Suttorp, and N. Hansen, Steady-State Selection and Efficient Covariance Matrix Update in the Multi-objective CMA-ES, Fourth International Conference on Evolutionary Multi-Criterion Optimization, 2007.
DOI : 10.1007/978-3-540-70928-2_16

S. Kern, S. D. Müller, N. Hansen, D. Büche, J. Ocenasek et al., Learning probability distributions in continuous evolutionary algorithms ??? a comparative review, Natural Computing, vol.3, issue.1, pp.77-112, 2004.
DOI : 10.1023/B:NACO.0000023416.59689.4e

I. Rechenberg, Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Frommann-Holzboog, 1973.

T. Suttorp, N. Hansen, and C. Igel, Efficient covariance matrix update for variable metric evolution strategies, Machine Learning, pp.167-197, 2009.
DOI : 10.1007/s10994-009-5102-1

URL : https://hal.archives-ouvertes.fr/inria-00369468

L. G. Valiant, The complexity of computing the (µ+?)-MO-CMA-ES I (µ+?)-MO-CMA-ES P (µ+1)-MO-CMA-ES I (µ+1)-MO-CMA-ES P NSGA-II Two-objective functions ZDT1 6.69909 V 6.71001 I,III,V 6.69899 I,V 6, p.69625

T. Voß, N. Beume, G. Rudolph, and C. Igel, Scalarization versus indicator-based selection in multi-objective CMA evolution strategies, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp.3041-3048, 2008.
DOI : 10.1109/CEC.2008.4631208

T. Voß, N. Hansen, and C. Igel, Recombination for learning strategy parameters in the MO-CMA-ES, Fifth International Conference on Evolutionary Multi-Criterion Optimization, 2009.

L. While, A New Analysis of the LebMeasure Algorithm for Calculating Hypervolume, Third International Conference on Evolutionary Multi-Criterion Optimization, pp.326-340, 2005.
DOI : 10.1007/978-3-540-31880-4_23

E. Zitzler, Hypervolume metric calculation, Swiss Federal Institute of Technology (ETH) Zurich, 2001.

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

E. Zitzler and L. Thiele, Multiobjective optimization using evolutionary algorithms ??? A comparative case study, Fifth International Conference on Parallel Problem Solving from Nature (PPSN-V), volume 1498 of LNCS, pp.292-301
DOI : 10.1007/BFb0056872

E. Zitzler, L. Thiele, M. Laumanns, C. M. Fonseca, V. Grunert et al., Performance assessment of multiobjective optimizers: an analysis and review, IEEE Transactions on Evolutionary Computation, vol.7, issue.2, pp.117-132, 2003.
DOI : 10.1109/TEVC.2003.810758