B. Bischl, ASlib: A benchmark library for algorithm selection, Artificial Intelligence, vol.237, 2015.
DOI : 10.1016/j.artint.2016.04.003

J. Branke, K. Deb, H. Dierolf, and M. Osswald, Finding Knees in Multi-objective Optimization, pp.722-731, 2004.
DOI : 10.1007/978-3-540-30217-9_73

I. Das, On characterizing the ?knee? of the Pareto curve based on Normal-Boundary Intersection, Structural Optimization, vol.8, issue.2-3, pp.107-115, 1999.
DOI : 10.1007/BF01195985

K. Deb, Multi-objective Evolutionary Algorithms: Introducing Bias Among Pareto-optimal Solutions, Advances in evolutionary computing, pp.263-292
DOI : 10.1007/978-3-642-18965-4_10

M. Gagliolo and J. Schmidhuber, Algorithm portfolio selection as a bandit problem with unbounded losses, Annals of Mathematics and Artificial Intelligence, vol.32, issue.1, pp.49-86, 2011.
DOI : 10.1007/s10472-011-9228-z

C. P. Gomes and B. Selman, Algorithm portfolios, Artificial Intelligence, vol.126, issue.1-2, pp.43-62, 2001.
DOI : 10.1016/S0004-3702(00)00081-3

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

V. Heidrich-meisner and C. Igel, Hoeffding and Bernstein races for selecting policies in evolutionary direct policy search, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, pp.401-408, 2009.
DOI : 10.1145/1553374.1553426

B. A. Huberman, R. M. Lukose, and T. Hogg, An Economics Approach to Hard Computational Problems, Science, vol.275, issue.5296, pp.51-54, 1997.
DOI : 10.1126/science.275.5296.51

S. Kadioglu, Y. Malitsky, A. Sabharwal, H. Samulowitz, and M. Sellmann, Algorithm Selection and Scheduling, Proc. 17th CP, pp.454-469, 2011.
DOI : 10.1007/978-3-642-23786-7_35

L. Kotthoff, ICON challenge on algorithm selection, p.4326, 2015.

K. Leyton-brown, E. Nudelman, G. Andrew, J. Mcfadden, and Y. Shoham, A portfolio approach to algorithm selection, Proc. IJCAI, pp.1542-1543, 2003.

Y. Malitsky, A. Sabharwal, H. Samulowitz, and M. Sellmann, Algorithm portfolios based on cost-sensitive hierarchical clustering, Proc. 23rd IJCAI, pp.608-614, 2013.

M. M?s?r and M. Sebag, Algorithm selection as a collaborative filtering problem, Tech. rep, 2013.

E. O´mahonyo´mahony, E. Hebrard, A. Holland, C. Nugent, and B. O´sullivano´sullivan, Using case-based reasoning in an algorithm portfolio for constraint solving, Proc. ICAICS, pp.210-216, 2008.

R. J. Oentaryo, S. D. Handoko, and H. C. Lau, Algorithm selection via ranking, Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI), 2015.

F. Pedregosa, Scikit-learn: Machine learning in Python, Journal of Machine Learning Research, vol.12, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

J. R. Rice, The Algorithm Selection Problem, Advances in Computers, vol.15, pp.65-118, 1976.
DOI : 10.1016/S0065-2458(08)60520-3

D. Stern, R. Herbrich, T. Graepel, H. Samulowitz, L. Pulina et al., Collaborative expert portfolio management, Proc. 24th AAAI, pp.179-184, 2010.

D. Wolpert and W. Macready, No free lunch theorems for optimization, IEEE Transactions on Evolutionary Computation, vol.1, issue.1, pp.67-82, 1997.
DOI : 10.1109/4235.585893

L. Xu, F. Hutter, H. Hoos, K. Leyton-brown, L. Xu et al., Features for SAT (2012), university of British Columbia 22 Satzilla: portfolio-based algorithm selection for sat, Journal of Artificial Intelligence Research, pp.565-606, 2008.

L. Xu, F. Hutter, J. Shen, H. H. Hoos, and K. Leyton-brown, Satzilla2012: improved algorithm selection based on cost-sensitive classification models, Balint et al.Balint et al, pp.57-58, 2012.

X. Yun and S. L. Epstein, Learning Algorithm Portfolios for Parallel Execution, Proc LION 6, pp.323-338, 2012.
DOI : 10.1007/978-3-642-34413-8_23