I. Abío and P. J. Stuckey, Encoding Linear Constraints into SAT, CP, pp.75-91, 2014.
DOI : 10.1007/978-3-319-10428-7_9

A. Aggoun and N. Beldiceanu, Extending chip in order to solve complex scheduling and placement problems, JFPL, p.51, 1992.
DOI : 10.1016/0895-7177(93)90068-A
URL : https://hal.archives-ouvertes.fr/hal-00442821

N. S. Altman, An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression. The American Statistician, pp.175-185, 1992.

R. Amadini, M. Gabbrielli, and J. Mauro, An Empirical Evaluation of Portfolios Approaches for Solving CSPs, CPAIOR, pp.316-324, 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, pp.1357-1359, 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, LION, pp.21-35, 2014.
DOI : 10.1007/978-3-319-09584-4_3
URL : https://hal.archives-ouvertes.fr/hal-01088429

R. Amadini, M. Gabbrielli, and J. Mauro, Abstract, Theory and Practice of Logic Programming, vol.41, issue.4-5, pp.509-524, 2014.
DOI : 10.1007/s10601-008-9051-2

R. Amadini, M. Gabbrielli, and J. Mauro, SUNNY-CP, Proceedings of the 30th Annual ACM Symposium on Applied Computing, SAC '15, 2015.
DOI : 10.1145/2695664.2695741
URL : https://hal.archives-ouvertes.fr/hal-01227589

R. Amadini and P. J. Stuckey, Sequential Time Splitting and Bounds Communication for a Portfolio of Optimization Solvers, CP, pp.108-124, 2014.
DOI : 10.1007/978-3-319-10428-7_11
URL : https://hal.archives-ouvertes.fr/hal-01091664

C. Ansótegui, M. Sellmann, and K. Tierney, A Gender-Based Genetic Algorithm for the Automatic Configuration of Algorithms, CP, pp.142-157, 2009.
DOI : 10.1007/BF02430370

A. Arbelaez, Y. Hamadi, and M. Sebag, Continuous Search in Constraint Programming, ICTAI, pp.53-60, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00515137

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

G. Audemard, B. Hoessen, S. Jabbour, J. Lagniez, and C. Piette, PeneLoPe, a Parallel Clause-Freezer Solver, SAT Challenge 2012, pp.43-44, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00865592

F. Azevedo, Cardinal: A Finite Sets Constraint Solver, Constraints, vol.12, issue.1, pp.93-129, 2007.
DOI : 10.1007/s10601-006-9012-6

P. Barahona, S. Hölldobler, and V. Nguyen, Representative Encodings to Translate Finite CSPs into SAT, CPAIOR, pp.251-267, 2014.
DOI : 10.1007/978-3-319-07046-9_18

C. W. Barrett, R. Sebastiani, S. A. Seshia, and C. Tinelli, Satisfiability Modulo Theories, Handbook of Satisfiability, pp.825-885, 2009.
URL : https://hal.archives-ouvertes.fr/hal-01095009

R. Barták, History of Constraint Programming, 2010.
DOI : 10.1002/9780470400531.eorms0382

R. Battiti and M. Protasi, Approximate Algorithms and Heuristics for MAX-SAT, Handbook of Combinatorial Optimization, pp.77-148
DOI : 10.1007/978-1-4613-0303-9_2

R. Becket, Specification of FlatZinc ? Version 1.6, 2014.

M. Said-belaid, C. Michel, and M. Rueher, Boosting Local Consistency Algorithms over Floating-Point Numbers, CP, CP'12, pp.127-140, 2012.
DOI : 10.1007/978-3-642-33558-7_12

N. Beldiceanu, M. Carlsson, S. Demassey, and T. Petit, Global Constraint Catalogue: Past, Present and Future, Constraints, vol.2, issue.1, pp.21-62, 2007.
DOI : 10.1007/s10601-006-9010-8

T. Berthold, Measuring the impact of primal heuristics. Operations Re- Search Letters, pp.611-614, 2013.

C. Bessiere, Z. Kiziltan, A. Rappini, and T. Walsh, A Framework for Combining Set Variable Representations, 2013.
URL : https://hal.archives-ouvertes.fr/lirmm-01067263

S. Bistarelli, U. Montanari, and F. Rossi, Constraint Solving over Semi-rings, IJCAI, pp.624-630, 1995.

S. Bistarelli, U. Montanari, and F. Rossi, Semiring-based constraint satisfaction and optimization, Journal of the ACM, vol.44, issue.2, pp.201-236, 1997.
DOI : 10.1145/256303.256306
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.4.3513

S. Bistarelli, U. Montanari, F. Rossi, and T. Schiex, Gérard Verfaillie, andHéì ene Fargier. Semiring-Based CSPs and Valued CSPs: Frameworks , Properties, and Comparison, Constraints, vol.4, issue.3, pp.199-240, 1999.
DOI : 10.1023/A:1026441215081

B. Botella, A. Gotlieb, and C. Michel, Symbolic execution of floating-point computations, Software Testing, Verification and Reliability, vol.14, issue.2, pp.97-121, 2006.
DOI : 10.1002/stvr.333
URL : https://hal.archives-ouvertes.fr/inria-00540299

S. Boyd and L. Vandenberghe, Convex Optimization, 2004.

P. Brucker, A. Drexl, R. Möhring, K. Neumann, and E. Pesch, Resource-constrained project scheduling: Notation, classification, models, and methods, European Journal of Operational Research, vol.112, issue.1, pp.3-41, 1999.
DOI : 10.1016/S0377-2217(98)00204-5

M. Hans-kleine-buning, A. Karpinski, and . Flogel, Resolution for Quantified Boolean Formulas, Information and Computation, vol.117, issue.1, pp.12-18, 1995.
DOI : 10.1006/inco.1995.1025

T. Carchrae, Low Knowledge Algorithm Control for Constraint-Based Scheduling, 2009.

T. Carchrae and J. Beck, Low-Knowledge Algorithm Control, AAAI, pp.49-54, 2004.

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

Y. Caseau, F. Laburthe, and G. Silverstein, A Meta-Heuristic Factory for Vehicle Routing Problems, CP, pp.144-158, 1999.
DOI : 10.1007/978-3-540-48085-3_11

A. Cesta, A. Oddi, F. Stephen, and . Smith, A Constraint-Based Method for Project Scheduling with Time Windows, Journal of Heuristics, vol.8, issue.1, pp.109-136, 2002.
DOI : 10.1023/A:1013617802515

V. Nitesh, K. W. Chawla, L. O. Bowyer, W. P. Hall, and . Kegelmeyer, SMOTE: Synthetic Minority Over-sampling Technique, Journal of Artificial Intelligence ReSearch, vol.16, 2002.

D. A. Cohen, P. Jeavons, C. Jefferson, K. E. Petrie, and B. M. Smith, Symmetry Definitions for Constraint Satisfaction Problems, Constraints, vol.129, issue.2-3, pp.115-137, 2006.
DOI : 10.1007/s10601-006-8059-8

. Toint, Convergence Properties of an Augmented Lagrangian Algorithm for Optimization with a Combination of General Equality and Linear Constraints

A. Stephen and . Cook, The Complexity of Theorem-Proving Procedures, STOC, pp.151-158, 1971.

J. Crawford, M. Ginsberg, E. Luks, and A. Roy, Symmetry-Breaking Predicates for Search Problems, pp.148-159, 1996.

B. George, J. H. Dantzig, and . Ramser, The Truck Dispatching Problem, Management science, vol.6, issue.1, pp.80-91, 1959.

V. Bruno-de-backer, P. Furnon, P. Shaw, P. Kilby, and . Prosser, Solving Vehicle Routing Problems using Constraint Programming and MetaHeuristics, Journal of Heuristics, vol.6, issue.4, pp.501-523, 2000.
DOI : 10.1023/A:1009621410177

R. Dechter, Enhancement schemes for constraint processing: Backjumping, learning, and cutset decomposition, Artificial Intelligence, vol.41, issue.3, pp.273-312, 1990.
DOI : 10.1016/0004-3702(90)90046-3

R. Dechter, Bucket elimination: A unifying framework for reasoning, Artificial Intelligence, vol.113, issue.1-2, pp.41-85, 1999.
DOI : 10.1016/S0004-3702(99)00059-4

R. Dechter, Constraint Processing, 2003.

G. Dooms, Y. Deville, and P. Dupont, CP(Graph): Introducing a Graph Computation Domain in Constraint Programming, CP2005 Proceedings, pp.211-225, 2005.
DOI : 10.1007/11564751_18

A. Dovier, C. Piazza, E. Pontelli, and G. Rossi, Sets and constraint logic programming, ACM Transactions on Programming Languages and Systems, vol.22, issue.5, pp.861-931, 2000.
DOI : 10.1145/365151.365169
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.132.1779

D. Dubois, H. Héì-ene-fargier, and . Prade, Fuzzy constraints in job-shop scheduling, Journal of Intelligent Manufacturing, vol.2, issue.1, pp.215-234, 1995.
DOI : 10.1007/BF00128646

D. Dubois and H. Prade, Fuzzy Sets and Systems -Theory and Applications . Academic press, 1980.

R. Ewald, Automatic Algorithm Selection for Complex Simulation Problems
DOI : 10.1007/978-3-8348-8151-9

T. Fahle, S. Schamberger, and M. Sellmann, Symmetry Breaking, CP, CP '01, pp.93-107, 2001.
DOI : 10.1007/3-540-45578-7_7

H. Fargier, J. Lang, and T. Schiex, Selecting Preferred Solutions in Fuzzy Constraint Satisfaction Problems, Proc. of the 1 s t European Congress on Fuzzy and Intelligent Technologies, 1993.

J. Antonio, P. M. Fernández, and . Hill, An Interval Constraint Branching Scheme for Lattice Domains, Journal of Universal Computer Science, vol.12, issue.11, pp.1466-1499, 2006.

P. Flener and J. Pearson, Introducing esra, a Relational Language for Modelling Combinatorial Problems, Proceedings of LOPSTR '03: Revised Selected Papers, pp.214-232, 2004.
DOI : 10.1007/978-3-540-25938-1_18

M. Merrill and . Flood, The Traveling-Salesman Problem, Operations ReSearch, vol.4, issue.1, pp.61-75, 1956.

F. Focacci, F. Laburthe, and A. Lodi, Local Search and Constraint Programming, Constraint and Integer Programming, pp.293-329
DOI : 10.1007/978-1-4419-8917-8_9
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.5.4401

E. C. Freuder, Synthesizing constraint expressions, Communications of the ACM, vol.21, issue.11, pp.958-966, 1978.
DOI : 10.1145/359642.359654
URL : http://dspace.mit.edu/handle/1721.1/6247#files-area

C. Eugene, R. J. Freuder, and . Wallace, Partial Constraint Satisfaction, Artificial Intelligence, vol.58, pp.21-70, 1992.

A. M. Frisch, W. Harvey, C. Jefferson, B. Martínez-hernández, and I. Miguel, Essence: A constraint language for specifying combinatorial problems, Constraints, vol.12, issue.1, pp.268-306, 2008.
DOI : 10.1007/s10601-008-9047-y

M. Gagliolo, Online Dynamic Algorithm Portfolios, 2010.

H. Gallaire, Logic Programming: Further Developments, SLP, pp.88-96, 1985.

J. Gary and G. , Performance Measurement and Analysis of Certain Search Algorithms, 1979.

C. Gebruers, A. Guerri, B. Hnich, and M. Milano, Making Choices Using Structure at the Instance Level within a??Case Based Reasoning Framework, CPAIOR, pp.380-386, 2004.
DOI : 10.1007/978-3-540-24664-0_27

G. , A. Open, and . Free, Efficient Constraint Solving Toolkit

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

P. Ian, B. M. Gent, and . Smith, Symmetry Breaking in Constraint Programming, Proceedings of ECAI-2000, pp.599-603, 2000.

P. Ian, T. Gent, and . Walsh, CSPLIB: A Benchmark Library for Constraints

G. Chu and P. J. Stuckey, Chuffed solver description, 2014.

F. Glover and M. Laguna, Tabu Search, 1999.
URL : https://hal.archives-ouvertes.fr/hal-01389283

K. Golden and W. Pang, Constraint Reasoning over Strings, Proceedings of the 9th International Conference on the Principles and Practices of Constraint Programming, pp.377-391, 2003.
DOI : 10.1007/978-3-540-45193-8_26

C. P. Gomes, H. Kautz, A. Sabharwal, and B. Selman, Chapter 2 Satisfiability Solvers, Satisfiability Solvers. Handbook of Knowledge Representation, vol.3, pp.89-134, 2008.
DOI : 10.1016/S1574-6526(07)03002-7

C. P. Gomes and B. Selman, Practical Aspects of Algorithm Portfolio Design

C. P. Gomes and B. Selman, Algorithm Portfolio Design: Theory vs. Practice, Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence, pp.190-197, 1997.

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

M. Grotschel and L. Lovász, Combinatorial Optimization. Handbook of combinatorics, pp.1541-1597, 1995.

J. Gu, P. W. Purdom, J. Franco, and B. W. Wah, Algorithms for the Satisfiability (SAT) Problem, DIMACS Series in Discrete Mathematics and Theoretical Computer Science, pp.19-152, 1996.
DOI : 10.1007/978-1-4757-3023-4_7

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

W. Robert, P. E. Haessler, and . Sweeney, Cutting Stock Problems and Solution Procedures, European Journal of Operational ReSearch, vol.54, issue.2, pp.141-150, 1991.

G. Hamerly and C. Elkan, Learning the k in k-means, NIPS, 2004.

M. Robert, G. L. Haralick, and . Elliott, Increasing Tree Search Efficiency for Constraint Satisfaction Problems, Artif. Intell, vol.14, issue.3, pp.263-313, 1980.

P. Hawkins, V. Lagoon, and P. J. Stuckey, Set Bounds and (Split) Set Domain Propagation Using ROBDDs, Australian Conference on Artificial Intelligence, pp.706-717, 2004.
DOI : 10.1007/978-3-540-30549-1_61
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.68.2433

P. Hawkins, J. Peter, and . Stuckey, A Hybrid BDD and SAT Finite Domain Constraint Solver, Practical Aspects of Declarative Languages, pp.103-117, 2006.
DOI : 10.1007/11603023_8

M. Hazewinkel, Encyclopaedia of mathematics, 1988.

E. Hebrard, O. Eoin, . Mahony, O. Barry, and . Sullivan, Constraint Programming and Combinatorial Optimisation in Numberjack, CPAIOR-10, pp.181-185, 2010.
DOI : 10.1007/978-3-642-13520-0_22
URL : https://hal.archives-ouvertes.fr/hal-00561698

C. Holzbaur, OFAI clp(q, r) Manual, Edition 1.3.3. Austrian ReSearch Institute for Artificial Intelligence, 1995.

J. N. Hooker, Integrated Methods for Optimization (International Series in Operations ReSearch & Management Science), 2006.

H. Hoos, M. T. Lindauer, and T. Schaub, Advances in Algorithm Selection for Answer Set Programming, pp.569-585, 2014.

H. Holger, B. Hoos, T. Kaufmann, M. Schaub, and . Schneider, Robust Benchmark Set Selection for Boolean Constraint Solvers, LNCS, vol.7997, pp.138-152, 2013.

A. Bernardo, R. M. Huberman, T. Lukose, and . Hogg, An Economics approach to Hard Computational Problems, Science, vol.275, issue.5296, pp.51-54, 1997.

B. Hurley, L. Kotthoff, Y. Malitsky, O. Barry, and . Sullivan, Proteus: A Hierarchical Portfolio of Solvers and Transformations, CPAIOR, pp.301-317, 2014.
DOI : 10.1007/978-3-319-07046-9_22

F. Hutter, Automated Configuration of Algorithms for Solving Hard Computational Problems, 2009.

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

F. 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.

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

D. S. Johnson and M. A. Trick, Cliques, Coloring, and Satisfiability: Second DIMACS Implementation Challenge, 1996.

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.

G. Katsirelos and F. Bacchus, Generalized NoGoods in CSPs, AAAI, pp.390-396, 2005.

Z. Kiziltan, L. Mandrioli, J. Mauro, O. Barry, and . Sullivan, A Classification-Based Approach to Managing a Solver Portfolio for CSPs, AICS, 2011.

Z. Kiziltan and T. Walsh, Constraint Programming with Multisets, pp.2002-2003

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

L. Kotthoff, Hybrid Regression-Classification Models for Algorithm Selection, ECAI, pp.480-485, 2012.

L. Kotthoff, On Algorithm Selection, with an application to combinatorial search problems, Constraints, vol.20, issue.4, 2012.
DOI : 10.1007/s10601-015-9214-x

L. Kotthoff, LLAMA: Leveraging Learning to Automatically Manage Algorithms, 2013.

L. Kotthoff, I. P. Gent, and I. Miguel, An Evaluation of Machine Learning in Algorithm Selection for Search Problems, pp.257-270, 2012.

C. Kroer and Y. Malitsky, Feature Filtering for Instance-Specific Algorithm Configuration, 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence, pp.849-855, 2011.
DOI : 10.1109/ICTAI.2011.132

V. Kumar, Algorithms for Constraint-Satisfaction Problems: A Survey, pp.32-44, 1992.

H. Ailsa, A. G. Land, and . Doig, An Automatic Method of Solving Discrete Programming Problems, Econometrica: Journal of the Econometric Society, pp.497-520, 1960.

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

V. Lifschitz, What Is Answer Set Programming?, 2008.

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, Instance-Specific Algorithm Configuration, 2012.
DOI : 10.1007/978-3-319-11230-5_3

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

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

Y. Malitsky, A. Sabharwal, H. Samulowitz, and M. Sellmann, Boosting Sequential Solver Portfolios: Knowledge Sharing and Accuracy Prediction, pp.153-167, 2013.
DOI : 10.1007/978-3-642-44973-4_17

Y. Malitsky and M. Sellmann, Instance-Specific Algorithm Configuration as a Method for Non-Model-Based Portfolio Generation, CPAIOR, 2012.
DOI : 10.1007/978-3-642-29828-8_16

K. Marriott, N. Nethercote, R. Rafeh, P. J. Stuckey, M. Garcia-de-la-banda et al., The Design of the Zinc Modelling Language, Constraints, vol.84, issue.1, pp.229-267, 2008.
DOI : 10.1007/s10601-008-9041-4

P. John, . Mcdermott, . J. Nils, and . Nilsson, Principles of Artificial lntelligence, Artif. Intell, vol.15, issue.12, pp.127-131, 1980.

C. Michel, M. Rueher, and Y. Lebbah, Solving Constraints over Floating-Point Numbers, 2001.
DOI : 10.1007/3-540-45578-7_36

M. Milano and M. Wallace, Integrating Operations Research in Constraint Programming, Annals of Operations Research, vol.11, issue.3, pp.37-76, 2010.
DOI : 10.1007/s10479-009-0654-9

M. Tom and . Mitchell, Machine Learning. McGraw Hill series in computer science, 1997.

M. Morara, J. Mauro, and M. Gabbrielli, Solving XCSP Problems by using Gecode, CILC, 2011.

M. W. Moskewicz, C. F. Madigan, Y. Zhao, L. Zhang, and S. Malik, Chaff, Proceedings of the 38th conference on Design automation , DAC '01, pp.530-535, 2001.
DOI : 10.1145/378239.379017

G. Katta and . Murty, Linear Complementarity, Linear and Nonlinear Programming, 1988.

C. John and . Nash, The (Dantzig) Simplex Method for Linear Programming, Computing in Science & Engineering, vol.2, issue.1, pp.29-31, 2000.

N. Nethercote, Converting MiniZinc to FlatZinc -Version 1.6 http

N. Nethercote, P. J. Stuckey, R. Becket, S. Brand, G. J. Duck et al., MiniZinc: Towards a Standard CP Modelling Language, LNCS, vol.4741, pp.529-543, 2007.
DOI : 10.1007/978-3-540-74970-7_38

M. Nikolic, F. Maric, and P. Janicic, Instance-Based Selection of Policies for SAT Solvers, SAT, pp.326-340
DOI : 10.1007/978-3-540-74970-7_50

W. Nuijten and C. L. Pape, Constraint-Based Job Shop Scheduling with IILOG SCHEDULER, Journal of Heuristics, vol.3, issue.4, pp.271-286, 1998.
DOI : 10.1023/A:1009687210594

O. Ohrimenko, J. Peter, M. Stuckey, and . Codish, Propagation via lazy clause generation, Constraints, vol.37, issue.1???3, pp.357-391, 2009.
DOI : 10.1007/s10601-008-9064-x
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.145.8691

O. Eoin, E. Mahony, A. Hebrard, C. Holland, . Nugent et al., Using case-based reasoning in an algorithm portfolio for constraint solving, AICS, vol.08, 2009.

G. Pesant and M. Gendreau, A view of local search in constraint programming, CP, pp.353-366, 1996.
DOI : 10.1007/3-540-61551-2_86

A. Florian, . Potra, J. Stephen, and . Wright, Interior-Point Methods, Journal of Computational and Applied Mathematics, vol.124, issue.1, pp.281-302, 2000.

L. Pulina and A. Tacchella, A Multi-engine Solver for Quantified Boolean Formulas, CP, pp.574-589, 2007.
DOI : 10.1007/978-3-540-74970-7_41

L. Pulina and A. Tacchella, A self-adaptive multi-engine solver for quantified Boolean formulas, Constraints, vol.2, issue.1, pp.80-116, 2009.
DOI : 10.1007/s10601-008-9051-2

J. Régin, Generalized Arc Consistency for Global Cardinality Constraint, AAAI, pp.209-215, 1996.

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

M. Colva, I. P. Roney-dougal, T. Gent, S. Kelsey, and . Linton, Tractable Symmetry Breaking Using Restricted Search Trees, ECAI, pp.211-215, 2004.

O. Roussel and C. Lecoutre, XML Representation of Constraint Networks: Format XCSP 2.1. CoRR, abs/0902, p.2362, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00872825

A. Sabharwal and H. Samulowitz, Insights into Parallelism with Intensive Knowledge Sharing, CP, pp.655-671, 2014.
DOI : 10.1007/978-3-319-10428-7_48

A. Sadler and C. Gervet, Enhancing set constraint solvers with lexicographic bounds, Journal of Heuristics, vol.47, issue.34, pp.23-67, 2008.
DOI : 10.1007/s10732-007-9028-0

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

T. Schiex, Possibilistic Constraint Satisfaction Problems or ???How to handle soft constraints????, Proc. 8th Conf. of Uncertainty in AI, pp.269-275, 1992.
DOI : 10.1016/B978-1-4832-8287-9.50041-4

T. Schiex, H. Fargier, and G. Verfaillie, Valued Constraint Satisfaction Problems: Hard and Easy Problems, IJCAI, pp.631-637

A. Schrijver, Combinatorial Optimization: Polyhedra and Efficiency, 2003.

A. Schutt, T. Feydy, J. Peter, . Stuckey, G. Mark et al., Solving RCPSP/max by lazy clause generation, Journal of Scheduling, vol.16, issue.3, pp.273-289, 2013.
DOI : 10.1007/s10951-012-0285-x

B. Selman and C. P. Gomes, Hill-Climbing Search. Encyclopedia of Cognitive Science
DOI : 10.1002/0470018860.s00015

B. Selman, J. Hector, . Levesque, G. David, and . Mitchell, A New Method for Solving Hard Satisfiability Problems, AAAI, pp.440-446, 1992.

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

K. Stergiou, Heuristics for Dynamically Adapting Propagation in Constraint Satisfaction Problems, AI Commun, vol.22, issue.3, pp.125-141, 2009.

M. Stojadinovic and F. Maric, Instance-Based Selection of CSP Solvers using Short Training, Pragmatics of SAT, 2014.

M. Stojadinovic and F. Maric, meSAT: multiple encodings of CSP to SAT, Constraints, vol.32, issue.2, pp.380-403, 2014.
DOI : 10.1007/s10601-014-9165-7

M. Streeter, Using Online Algorithms to solve NP-hard Problems More Efficiently in Practice, 2007.

J. Peter, R. Stuckey, J. Becket, and . Fischer, Philosophy of the MiniZinc challenge, Constraints, vol.15, issue.3, pp.307-316, 2010.

J. Peter, T. Stuckey, A. Feydy, G. Schutt, J. Tack et al., The MiniZinc Challenge, pp.55-60, 2008.

N. Tamura, A. Taga, S. Kitagawa, and M. Banbara, Compiling finite linear CSP into SAT, Constraints, vol.64, issue.2, pp.254-272, 2009.
DOI : 10.1007/s10601-008-9061-0
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.146.5103

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

P. Van and H. , The OPL Optimization Programming Language, 1999.

T. Walsh, Consistency and Propagation with Multiset Constraints: A Formal Viewpoint, CP, 2003.
DOI : 10.1007/978-3-540-45193-8_49

C. Wei-hsu, C. Chung-chang, and C. Lin, A practical guide to support vector classification, 2010.

H. David and . Wolpert, The Lack of a Priori Distinctions Between Learning Algorithms, Neural Computation, vol.8, issue.7, pp.1341-1390, 1996.

H. David, W. G. Wolpert, and . Macready, No Free Lunch Theorems for Optimization, Evolutionary Computation IEEE Transactions on, vol.1, issue.1, pp.67-82, 1997.

L. Xu, H. Hoos, and K. Leyton-brown, Hydra, Proceedings of the 2005 ACM workshop on Storage security and survivability , StorageSS '05, 2010.
DOI : 10.1145/1103780.1103797

L. Xu, F. Hutter, H. Hoos, and K. Leyton-brown, Evaluating Component Solver Contributions to Portfolio-Based Algorithm Selectors, SAT, 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

L. Xu, F. Hutter, H. H. Hoos, and K. Leyton-brown, SATzilla: Portfolio-Based Algorithm Selection for SAT, J. Artif. Intell. Res. (JAIR), vol.32, pp.565-606, 2008.

L. Xu, F. Hutter, H. H. Hoos, and K. Leyton-brown, Hydra- MIP: Automated Algorithm Configuration and Selection for Mixed Integer Programming, RCRA (workshop), 2011.

. Satzilla2012, Improved Algorithm Selection Based on Cost-Sensitive Classification Models. Solver description, SAT Challenge, 2012.