Machine Learning
Artificial Intelligence - special issue on computational tradeoffs under bounded resources
Scaling and Probabilistic Smoothing: Efficient Dynamic Local Search for SAT
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Fine-Tuning of Algorithms Using Fractional Experimental Designs and Local Search
Operations Research
Cross-disciplinary perspectives on meta-learning for algorithm selection
ACM Computing Surveys (CSUR)
Instance-Based Selection of Policies for SAT Solvers
SAT '09 Proceedings of the 12th International Conference on Theory and Applications of Satisfiability Testing
SATzilla: portfolio-based algorithm selection for SAT
Journal of Artificial Intelligence Research
A portfolio approach to algorithm select
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
SATenstein: automatically building local search SAT solvers from components
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
ParamILS: an automatic algorithm configuration framework
Journal of Artificial Intelligence Research
A gender-based genetic algorithm for the automatic configuration of algorithms
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
ISAC --Instance-Specific Algorithm Configuration
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Performance prediction and automated tuning of randomized and parametric algorithms
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
March_eq: implementing additional reasoning into an efficient look-ahead SAT solver
SAT'04 Proceedings of the 7th international conference on Theory and Applications of Satisfiability Testing
MiningZinc: a modeling language for constraint-based mining
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Instance-specific algorithm configuration generalizes both instance-oblivious algorithm tuning as well as algorithm portfolio generation. ISAC is a recently proposed non-model-based approach for tuning solver parameters dependent on the specific instance that needs to be solved. While ISAC has been compared with instance-oblivious algorithm tuning systems before, to date a comparison with portfolio generators and other instance-specific algorithm configurators is crucially missing. In this paper, among others, we provide a comparison with SATzilla, as well as three other algorithm configurators: Hydra, DCM and ArgoSmart. Our experimental comparison shows that non-model-based ISAC significantly outperforms prior state-of-the-art algorithm selectors and configurators. The following study was the foundation for the best sequential portfolio at the 2011 SAT Competition.