[. Arnold, A. Auger, N. Hansen, and Y. Ollivier, Information-geometric optimization algorithms: A unifying picture via invariance principles. arXiv preprint arXiv:1106, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00601503

A. Ahn, A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem, Information Sciences, vol.178, issue.1, pp.37-51, 2008.
DOI : 10.1016/j.ins.2007.07.024

[. Arbelaez, Y. Hamadi, and M. Sebag, Continuous search in constraint programming, 22nd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2010, pp.53-60, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00515137

A. Auger, N. Hansen, and M. Schoenauer, Benchmarking of Continuous Black Box Optimization Algorithms, Evolutionary Computation, vol.20, issue.4, p.481, 2012.
DOI : 10.1162/EVCO_e_00091

G. Audemard and L. Simon, Predicting learnt clauses quality in modern SAT solvers, Proceedings of the 21st International Joint Conference on Artifical Intelligence (IJCAI'09), pp.399-404

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

R. Bardenet, M. Brendel, B. Kégl, and M. Sebag, Collaborative hyperparameter tuning, Proceedings of the 30th International Conference on Machine Learning, ICML 2013 JMLR Proceedings, pp.199-207, 2013.
URL : https://hal.archives-ouvertes.fr/in2p3-00907381

I. Borg and P. J. Groenen, Modern Multidimensional Scaling: Theory and Applications, Journal of Educational Measurement, vol.40, issue.3, 2005.
DOI : 10.1007/BF02289341

C. G. Brazdil, C. Giraud-carrier, R. Soares, and . Vilalta, Metalearning -Applications to Data Mining. Cognitive Technologies, 2009.

S. [. Bennett and . Lanning, The netflix prize, Proceedings of of the 13th International Conference on Knowledge Discovery and Data Mining (KDD'07) Cup and Workshop, p.35, 2007.

M. [. Billsus and . Pazzani, Learning collaborative information filters, Proceedings of the 15th International Conference on Machine Learning (ICML'98), pp.46-54, 1998.

C. Soares, A comparison of ranking methods for classification algorithm selection, Machine Learning: ECML 2000, 11th European Conference on Machine Learning, pp.63-74, 2000.

S. Deerwester, S. T. Dumais, W. George, . Furnas, K. Thomas et al., Indexing by latent semantic analysis, Journal of the American Society for Information Science, vol.41, issue.6, pp.41391-407, 1990.
DOI : 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.108.8490

S. L. Epstein, E. C. Freuder, R. Wallace, A. Morozov, and B. Samuels, The Adaptive Constraint Engine, Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming, pp.525-540, 2006.
DOI : 10.1007/3-540-46135-3_35

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

E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten, The weka data mining software: an update, ACM SIGKDD Explorations Newsletter, vol.11, pp.10-18, 2009.

[. Hutter, Y. Hamadi, H. H. Hoos, and K. Leyton-brown, Performance Prediction and Automated Tuning of Randomized and Parametric Algorithms, Principles and Practice of Constraint Programming -CP 2006, 12th International Conference, pp.213-228, 2006.
DOI : 10.1007/11889205_17

H. [. Hutter, K. Hoos, and . Leyton-brown, Sequential Model-Based Optimization for General Algorithm Configuration, Learning and Intelligent Optimization, pp.507-523, 2011.
DOI : 10.1007/978-0-387-84858-7

H. Hutter, H. Holger, K. Hoos, T. Leyton-brown, and . Stützle, Paramils: an automatic algorithm configuration framework, Journal of Artificial Intelligence Research, vol.36, issue.1, pp.267-306, 2009.

[. Hutter, H. H. Hoos, K. Leyton-brown, and T. Stützle, ParamILS: An automatic algorithm configuration framework, J. Artif. Intell. Res. (JAIR), vol.36, pp.267-306, 2009.

M. Hofmann and R. Klinkenberg, RapidMiner: Data Mining Use Cases and Business Analytics Applications, Chapman & Hall/CRC Data Mining and Knowledge Discovery Series, 2013.

[. 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, p.51, 2009.
DOI : 10.1145/1553374.1553426

H. Holger and . Hoos, Programming by optimization, Commun. ACM, vol.55, issue.2, pp.70-80, 2012.

R. Hansen, N. Ros, M. Mauny, A. Schoenauer, and . Auger, Impacts of invariance in search: When CMA-ES and PSO face ill-conditioned and non-separable problems, Applied Soft Computing, vol.11, issue.8, pp.5755-5769, 2011.
DOI : 10.1016/j.asoc.2011.03.001

URL : https://hal.archives-ouvertes.fr/inria-00583669

[. Järvelin and J. Kekäläinen, Ir evaluation methods for retrieving highly relevant documents [Kal02] Alexandros Kalousis. Algorithm selection via meta-learning, SIGIR, pp.41-48, 2000.

[. Kalousis and M. Hilario, Representational issues in meta-learning, Machine Learning, Proceedings of the Twentieth International Conference (ICML 2003), pp.313-320, 2003.

Y. Kadioglu, A. Malitsky, H. Sabharwal, M. Samulowitz, and . Sellmann, Algorithm Selection and Scheduling, Principles and Practice of Constraint Programming (CP'11), pp.454-469, 2011.
DOI : 10.1007/978-3-642-23786-7_35

[. Melville and V. Sindhwani, Recommender systems, Encyclopedia of Machine Learning, vol.1, pp.829-838, 2010.

A. E. Volker-nannen and . Eiben, Relevance estimation and value calibration of evolutionary algorithm parameters, Proceedings of the 20th International Joint Conference on Artificial Intelligence, pp.975-980, 2007.

E. Ohh-+-08-]-e.-o-'mahony, A. Hebrard, C. Holland, B. Nugent, and . Osullivan, Using case-based reasoning in an algorithm portfolio for constraint solving, Irish Conference on Artificial Intelligence and Cognitive Science, 2008.

[. Pfahringer, H. Bensusan, and C. G. Giraud-carrier, Meta-learning by landmarking various learning algorithms, Proceedings of the Seventeenth International Conference on Machine Learning, pp.743-750, 2000.

]. J. [-ric76 and . Rice, The algorithm selection problem Advances in computers, pp.65-118, 1976.

N. P. Resnick, M. Iacovou, P. Suchak, and J. Bergstrom, GroupLens, Proceedings of the 1994 ACM conference on Computer supported cooperative work , CSCW '94, pp.175-186, 1994.
DOI : 10.1145/192844.192905

]. O. Rou11 and . Roussel, Description of ppfolio. Solver description www.cril. univ-artois.fr, 2011.

A. [. Sutton and . Barto, Reinforcement learning, 1998.
DOI : 10.1007/978-1-4615-3618-5

URL : https://hal.archives-ouvertes.fr/hal-00764281

D. H. Stern, R. Herbrich, and T. Graepel, Matchbox, Proceedings of the 18th international conference on World wide web, WWW '09, pp.111-120, 2009.
DOI : 10.1145/1526709.1526725

T. [. Su and . Khoshgoftaar, A Survey of Collaborative Filtering Techniques, Advances in Artificial Intelligence, vol.46, issue.2, p.19, 2009.
DOI : 10.1002/asi.10372

R. [. Samulowitz and . Memisevic, Learning to solve QBF, Proceedings of the National Conference on Artificial Intelligence (AAAI'07, pp.255-360, 2007.

B. Silverthorn and R. Miikkulainen, Latent class models for algorithm portfolio methods, Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, 2010.

J. [. Srebro, T. Rennie, and . Jaakkola, Maximum-margin matrix factorization Advances in neural information processing systems, pp.1329-1336, 2005.

S. [. Streeter and . Smith, New techniques for algorithm portfolio design, Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence (UAI'08), p.26, 2008.

D. H. Stern, H. Samulowitz, R. Herbrich, T. Graepel, L. Pulina et al., Collaborative expert portfolio management, Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2010, 2010.

[. Serban, J. Vanschoren, J. Kietz, and A. Bernstein, A survey of intelligent assistants for data analysis, ACM Computing Surveys, vol.45, issue.3, p.31, 2013.
DOI : 10.1145/2480741.2480748

[. Szepesvari, Algorithms for Reinforcement Learning, Synthesis Lectures on Artificial Intelligence and Machine Learning, vol.4, issue.1, 2010.
DOI : 10.2200/S00268ED1V01Y201005AIM009

[. Thornton, F. Hutter, H. H. Hoos, and K. Leyton-brown, Auto-WEKA, Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '13, 1208.
DOI : 10.1145/2487575.2487629

]. C. Tsvl07a, A. Teo, . Smola, Q. V. Vishwanathan, and . Le, A scalable modular convex solver for regularized risk minimization, Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.727-736, 2007.

[. Teo, A. J. Smola, S. V. Vishwanathan, and Q. V. Le, A scalable modular convex solver for regularized risk minimization, Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '07, pp.727-736, 2007.
DOI : 10.1145/1281192.1281270

[. Tamura, T. Tanjo, and M. Banbara, System description of a sat-based csp solver sugar, Proceedings of the 3rd International CSP Solver Competition, pp.71-75, 2008.

[. Villemonteix, E. Vázquez, M. Sidorkiewicz, and E. Walter, Global optimization of expensive-to-evaluate functions: an empirical comparison of two sampling criteria, Journal of Global Optimization, vol.10, issue.2, pp.373-389, 2009.
DOI : 10.1007/s10898-008-9313-y

URL : https://hal.archives-ouvertes.fr/hal-00354656

A. [. Weimer, Q. Karatzoglou, A. Le, and . Smola, Cofirank-maximum margin matrix factorization for collaborative ranking, Proceedings of the 21st Annual Conference on Neural Information Processing Systems (NIPS'07), pp.222-230, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00482740

F. [. Xu, H. H. Hutter, K. Hoos, and . Leyton-brown, SATzilla: portfoliobased algorithm selection for SAT, Journal of Artificial Intelligence Research, vol.32, issue.1, pp.565-606, 2008.

[. Xu, F. Hutter, H. Hoos, and K. Leyton-brown, Evaluating Component Solver Contributions to Portfolio-Based Algorithm Selectors, Theory and Applications of Satisfiability Testing -SAT 2012, pp.228-241, 2012.
DOI : 10.1007/978-3-642-31612-8_18

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.303.2384

[. Xu, F. Hutter, H. Hoos, K. Leyton-brown-yun, and S. L. Epstein, Features for SAT Learning algorithm portfolios for parallel execution, Proceedings of the 6th Learning and Intelligent OptimizatioN Conference (LION'12), pp.323-338, 2012.