R. Amadini, M. Gabbrielli, and J. Mauro, An Empirical Evaluation of Portfolios Approaches for Solving CSPs, CPAIOR, 2013.
DOI : 10.1007/978-3-642-38171-3_21
URL : https://hal.archives-ouvertes.fr/hal-00909297

R. Amadini, M. Gabbrielli, and J. Mauro, Features for Building CSP Portfolio Solvers, 2013.

R. Amadini, M. Gabbrielli, and J. Mauro, SUNNY: a Simple and Dynamic Algorithm Portfolio for Solving CSPs. CoRR, abs/1311, 2013.

S. Arlot and A. Celisse, A survey of cross-validation procedures for model selection, Statistics Surveys, vol.4, issue.0, pp.40-79, 2010.
DOI : 10.1214/09-SS054
URL : https://hal.archives-ouvertes.fr/hal-00407906

R. Becket, Specification of FlatZinc -Version 1.6

Y. Borenstein and R. Poli, Kolmogorov complexity, Optimization and Hardness, 2006 IEEE International Conference on Evolutionary Computation, pp.112-119, 2006.
DOI : 10.1109/CEC.2006.1688297
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.160.9057

T. Carchrae and J. Beck, APPLYING MACHINE LEARNING TO LOW-KNOWLEDGE CONTROL OF OPTIMIZATION ALGORITHMS, Computational Intelligence, vol.15, issue.6, pp.372-387, 2005.
DOI : 10.1287/ijoc.14.2.98.120

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

C. P. Gomes, B. Selman, and N. Crato, Heavy-tailed distributions in combinatorial search, CP, pp.121-135, 1997.
DOI : 10.1007/BFb0017434

H. Guo and W. H. Hsu, A machine learning approach to algorithm selection for $\mathcal{NP}$ -hard optimization problems: a case study on the MPE problem, Annals of Operations Research, vol.151, issue.3, pp.61-82, 2007.
DOI : 10.1007/s10479-007-0229-6

M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann et al., The WEKA data mining software, ACM SIGKDD Explorations Newsletter, vol.11, issue.1, 2009.
DOI : 10.1145/1656274.1656278

F. Hutter, L. Xu, H. H. Hoos, and K. Leyton-brown, Algorithm Runtime Prediction: The State of the Art, 1211.

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

S. Kadioglu, Y. Malitsky, M. Sellmann, and K. Tierney, ISAC - Instance-Specific Algorithm Configuration, ECAI, 2010.

J. D. Knowles and D. Corne, Towards Landscape Analyses to Inform the Design of Hybrid Local Search for the Multiobjective Quadratic Assignment Problem, HIS, pp.271-279, 2002.

L. Kotthoff, Algorithm Selection for Combinatorial Search Problems: A Survey. CoRR, abs, 1210.

K. Leyton-brown, E. Nudelman, and Y. Shoham, Learning the Empirical Hardness of Optimization Problems: The Case of Combinatorial Auctions, CP, pp.556-572, 2002.
DOI : 10.1007/3-540-46135-3_37

L. Lobjois and M. Lema??trelema??tre, Branch and Bound Algorithm Selection by Performance Prediction, AAAI/IAAI, pp.353-358, 1998.

A. K. Mackworth, Consistency in networks of relations, Consistency in Networks of Relations, pp.99-118, 1977.
DOI : 10.1016/0004-3702(77)90007-8

Y. Malitsky, A. Sabharwal, H. Samulowitz, and M. Sellmann, Algorithm Portfolios Based on Cost-Sensitive Hierarchical Clustering, IJCAI. IJCAI/AAAI, 2013.

P. Merz, Advanced Fitness Landscape Analysis and the Performance of Memetic Algorithms, Evolutionary Computation, vol.13, issue.4, pp.303-325, 2004.
DOI : 10.1109/4235.887236

E. Omahony, E. Hebrard, A. Holland, C. Nugent, and B. Osullivan, Using case-based reasoning in an algorithm portfolio for constraint solving, AICS, vol.08, 2009.

H. Samulowitz, C. Reddy, A. Sabharwal, and M. Sellmann, Snappy: A Simple Algorithm Portfolio, SAT, pp.422-428, 2013.
DOI : 10.1007/978-3-642-39071-5_33

K. Smith-miles, Cross-disciplinary perspectives on meta-learning for algorithm selection, ACM Computing Surveys, vol.41, issue.1, 2008.
DOI : 10.1145/1456650.1456656

O. Telelis and P. Stamatopoulos, Combinatorial optimization through statistical instance-based learning, Proceedings 13th IEEE International Conference on Tools with Artificial Intelligence. ICTAI 2001, pp.203-209, 2001.
DOI : 10.1109/ICTAI.2001.974466

L. Xu, F. Hutter, J. Shen, H. Hoos, and K. Leyton-brown, SATzilla2012: Improved algorithm selection based on cost-sensitive classification models. Solver description, SAT Challenge, 2012.

L. Xu, F. Hutter, H. H. Hoos, and K. Leyton-brown, SATzilla-07: The Design and Analysis of an Algorithm Portfolio for SAT, CP, 2007.
DOI : 10.1007/978-3-540-74970-7_50

L. Xu, F. Hutter, H. H. Hoos, and K. Leyton-brown, Hydra-MIP: Automated Algorithm Configuration and Selection for Mixed Integer Programming, RCRA workshop on Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion, 2011.