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, An enhanced features extractor for a portfolio of constraint solvers, Proceedings of the 29th Annual ACM Symposium on Applied Computing, SAC '14, 2014.
DOI : 10.1145/2554850.2555114
URL : https://hal.archives-ouvertes.fr/hal-01089183

R. Amadini, M. Gabbrielli, and J. Mauro, Portfolio Approaches for Constraint Optimization Problems
URL : https://hal.archives-ouvertes.fr/hal-01088429

R. Amadini, M. Gabbrielli, and J. Mauro, Abstract, ICLP, 2014.
DOI : 10.1007/s10601-008-9051-2

R. Amadini and P. J. Stuckey, Sequential Time Splitting and Bounds Communication for a Portfolio of Optimization Solvers, 2014.
DOI : 10.1007/978-3-319-10428-7_11
URL : https://hal.archives-ouvertes.fr/hal-01091664

G. Audemard, B. Hoessen, S. Jabbour, J. Lagniez, and C. Piette, PeneLoPe, a Parallel Clause-Freezer Solver, SAT Challenge, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00865592

Y. Chevaleyre, U. Endriss, J. Lang, and N. Maudet, A Short Introduction to Computational Social Choice, SOFSEM, 2007.
DOI : 10.1007/978-3-540-69507-3_4

B. De-cat, B. Bogaerts, J. Devriendt, and M. Denecker, Model Expansion in the Presence of Function Symbols Using Constraint Programming, ICTAI, 2013.

M. Gebser, R. Kaminski, B. Kaufmann, T. Schaub, M. T. Schneider et al., A Portfolio Solver for Answer Set Programming: Preliminary Report, LPNMR, 2011.
DOI : 10.1007/978-3-642-20895-9_40

A. V. Gelder, Careful Ranking of Multiple Solvers with Timeouts and Ties, SAT, 2011.
DOI : 10.1007/978-1-4615-4459-3

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

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, 2007.
DOI : 10.1007/s10479-007-0229-6

E. Hebrard, E. O. Mahony, and B. O. Sullivan, Constraint Programming and Combinatorial Optimisation in Numberjack, CPAIOR, 2010.
DOI : 10.1007/978-3-642-13520-0_22
URL : https://hal.archives-ouvertes.fr/hal-00561698

H. Hoos, M. T. Lindauer, and T. Schaub, Advances in algorithm selection for answer set programming, TPLP, vol.2, 2014.

H. H. Hoos, B. Kaufmann, T. Schaub, and M. Schneider, Robust Benchmark Set Selection for Boolean Constraint Solvers
DOI : 10.1007/978-3-642-44973-4_16

B. Hurley, L. Kotthoff, Y. Malitsky, and B. O. Sullivan, Proteus: A Hierarchical Portfolio of Solvers and Transformations, CPAIOR, 2014.
DOI : 10.1007/978-3-319-07046-9_22

F. Hutter, L. Xu, H. H. Hoos, and K. Leyton-brown, Algorithm Runtime Prediction: The State of the Art, 2012.

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.

L. Kotthoff, Algorithm Selection for Combinatorial Search Problems: A Survey, 2012.
DOI : 10.1007/978-3-642-31612-8_18

C. Kroer and Y. Malitsky, Feature Filtering for Instance-Specific Algorithm Configuration, 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence, 2011.
DOI : 10.1109/ICTAI.2011.132
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.210.9502

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

N. Nethercote, P. J. Stuckey, R. Becket, S. Brand, G. J. Duck et al., MiniZinc: Towards a Standard CP Modelling Language, CP, 2007.
DOI : 10.1007/978-3-540-74970-7_38
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.132.9321

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

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

O. Roussel and C. Lecoutre, XML Representation of Constraint Networks: Format XCSP 2.1. CoRR, 37] SAT Competition 2013 web site, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00872825

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

P. J. Stuckey, R. Becket, and J. Fischer, Philosophy of the MiniZinc challenge, Constraints, vol.15, issue.3, 2010.
DOI : 10.1007/s10601-010-9093-0

O. Telelis and P. Stamatopoulos, Combinatorial optimization through statistical instance-based learning, Proceedings 13th IEEE International Conference on Tools with Artificial Intelligence. ICTAI 2001, 2001.
DOI : 10.1109/ICTAI.2001.974466
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.156.2725

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