SUNNY for Algorithm Selection: A Preliminary Study, CILC, 2015. Available at ,
URL : https://hal.archives-ouvertes.fr/hal-01227595
Abstract, Theory and Practice of Logic Programming, vol.41, issue.4-5, pp.509-524, 2014. ,
DOI : 10.1007/s10601-008-9051-2
A multicore tool for constraint solving, IJCAI, pp.232-238, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01227592
Portfolio approaches for constraint optimization problems. AMAI, pp.1-18, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01088429
SUNNY-CP, Proceedings of the 30th Annual ACM Symposium on Applied Computing, SAC '15, pp.1861-1867, 2015. ,
DOI : 10.1145/2695664.2695741
URL : https://hal.archives-ouvertes.fr/hal-01227589
ASlib: A benchmark library for algorithm selection, Artificial Intelligence, vol.237, 2015. ,
DOI : 10.1016/j.artint.2016.04.003
Improved features for runtime prediction of domainindependent planners, ICAPS. AAAI, 2014. ,
Learning when to use lazy learning in constraint solving, ECAI, pp.873-878, 2010. ,
Minion: A fast scalable constraint solver, ECAI, pp.98-102, 2006. ,
Algorithm portfolios, Artificial Intelligence, vol.126, issue.1-2, pp.43-62, 2001. ,
DOI : 10.1016/S0004-3702(00)00081-3
An Introduction to Variable and Feature Selection, Journal of Machine Learning Research, vol.3, pp.1157-1182, 2003. ,
The WEKA data mining software, ACM SIGKDD Explorations Newsletter, vol.11, issue.1, 2009. ,
DOI : 10.1145/1656274.1656278
Correlation-based Feature Subset Selection for Machine Learning, 1998. ,
Very simple classification rules perform well on most commonly used datasets, Machine Learning, pp.63-91, 1993. ,
Advances in algorithm selection for answer set programming, TPLP, vol.2, issue.144-5, pp.569-585, 2014. ,
Proteus: A Hierarchical Portfolio of Solvers and Transformations, CPAIOR, pp.301-317, 2014. ,
DOI : 10.1007/978-3-319-07046-9_22
Sequential Model-Based Optimization for General Algorithm Configuration, LION, pp.507-523, 2011. ,
DOI : 10.1007/978-0-387-84858-7
Identifying Key Algorithm Parameters and Instance Features Using Forward Selection, LION, pp.364-381, 2013. ,
DOI : 10.1007/978-3-642-44973-4_40
Algorithm Runtime Prediction: The State of the Art, 1211. ,
ISAC -Instance- Specific Algorithm Configuration, ECAI, 2010. ,
A Practical Approach to Feature Selection, 9th International Workshop on Machine Learning, pp.249-256, 1992. ,
DOI : 10.1016/B978-1-55860-247-2.50037-1
Estimating attributes: Analysis and extensions of RELIEF, pp.171-182, 1994. ,
DOI : 10.1007/3-540-57868-4_57
Algorithm Selection for Combinatorial Search Problems: A Survey, AI Magazine, vol.17, issue.10, pp.48-60, 2014. ,
DOI : 10.1007/978-3-642-31612-8_18
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
Maxsat, hard and soft constraints, Handbook of Satisfiability, pp.613-631, 2009. ,
MiniZinc: Towards a Standard CP Modelling Language, CP, 2007. ,
DOI : 10.1007/978-3-540-74970-7_38
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
Overfitting in Making Comparisons Between Variable Selection Methods, Journal of Machine Learning Research, vol.3, pp.1371-1382, 2003. ,
The Algorithm Selection Problem, Advances in Computers, vol.15, pp.65-118, 1976. ,
DOI : 10.1016/S0065-2458(08)60520-3
An adaptation of relief for attribute estimation in regression, pp.296-304, 1997. ,
Cross-disciplinary perspectives on meta-learning for algorithm selection, ACM Computing Surveys, vol.41, issue.1, 2008. ,
DOI : 10.1145/1456650.1456656
Towards insightful algorithm selection for optimisation using meta-learning concepts, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pp.4118-4124, 2008. ,
DOI : 10.1109/IJCNN.2008.4634391
Heuristics for dynamically adapting propagation in constraint satisfaction problems, AI Commun, vol.22, issue.3, pp.125-141, 2009. ,
An Algorithm Selection Benchmark of the Container Pre-marshalling Problem, LION 9, pp.17-22, 2015. ,
DOI : 10.1007/978-3-319-19084-6_2
Hydra, Proceedings of the 2005 ACM workshop on Storage security and survivability , StorageSS '05, 2010. ,
DOI : 10.1145/1103780.1103797