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The Algorithm Selection Problem is concerned with selecting the best algorithm to solve a given problem on a case-by-case basis.
The algorithm selection problem
Rice, J. R. (1976) · 1976
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Perceptron trees: A case study in hybrid concept representations
Utgoff, P. E. (1988) · 1988
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The strength of weak learnability
Schapire, R. E. (1990) · 1990
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PRODIGY: an integrated architecture for planning and learning
Carbonell, J., Etzioni, O., Gil, Y., Joseph, R., Knoblock, C., Minton, S., and Veloso, M. (1991) · 1991
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Where the really hard problems are
Cheeseman, P., Kanefsky, B., and Taylor, W. M. (1991) · 1991
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Generalizing from case studies: A case study
Aha, D. W. (1992) · 1992
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COMPOSER: a probabilistic solution to the utility problem in Speed-Up learning
Gratch, J., and DeJong, G. (1992) · 1992
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Knowledge-based constraint-driven software synthesis
Smith, T. E., and Setliff, D. E. (1992) · 1992
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Stacked generalization
Wolpert, D. H. (1992) · 1992
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Addressing the selective superiority problem: Automatic Algorithm/Model class selection
Brodley, C. E. (1993) · 1993
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ODEXPERT: an expert system to select numerical solvers for initial value ODE systems
Kamel, M. S., Enright, W. H., and Ma, K. S. (1993) · 1993
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Knowledge-based algorithm construction for real-world engineering PDEs
Cahill, E. (1994) · 1994
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High-level optimization via automated statistical modeling
Brewer, E. A. (1995) · 1995
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For every generalization action, is there really an equal and opposite reaction? Analysis of the conservation law for generalization performance
Rao, R. B., Gordon, D., and Spears, W. (1995) · 1995
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An attempt to map the performance of a range of algorithm and heuristic combinations
Tsang, E. P. K., Borrett, J. E., and Kwan, A. C. M. (1995) · 1995
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Selecting the right heuristic algorithm: Runtime performance predictors
Allen, J. A., and Minton, S. (1996) · 1996
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Adaptive constraint satisfaction: The quickest first principle
Borrett, J. E., Tsang, E. P. K., and Walsh, N. R. (1996) · 1996
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Bagging predictors
Breiman, L. (1996) · 1996
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Phase transitions and the search problem
Hogg, T., Huberman, B. A., and Williams, C. P. (1996) · 1996
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Automatically configuring constraint satisfaction programs: A case study
Minton, S. (1996) · 1996
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An instance of adaptive constraint propagation
Sakkout, H. E., Wallace, M. G., and Richards, E. B. (1996) · 1996
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PYTHIA: a knowledge-based system to select scientific algorithms
Weerawarana, S., Houstis, E. N., Rice, J. R., Joshi, A., and Houstis, C. E. (1996) · 1996
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Maximizing the benefits of parallel search using machine learning
Cook, D. J., and Varnell, R. C. (1997) · 1997
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Statistical selection among Problem-Solving methods
Fink, E. (1997) · 1997
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An economics approach to hard computational problems
Huberman, B. A., Lukose, R. M., and Hogg, T. (1997) · 1997
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No free lunch theorems for optimization
Wolpert, D. H., and Macready, W. G. (1997) · 1997
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How to get a free lunch: A simple cost model for machine learning applications
Domingos, P. (1998) · 1998
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How to solve it automatically: Selection among Problem-Solving methods
Fink, E. (1998) · 1998
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Branch and bound algorithm selection by performance prediction
Lobjois, L., and Lemaître, M. (1998) · 1998
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An empirical comparison of voting classification algorithms: Bagging, boosting, and variants
Bauer, E., and Kohavi, R. (1999) · 1999
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A Meta-Heuristic factory for vehicle routing problems
Caseau, Y., Laburthe, F., and Silverstein, G. (1999) · 1999
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Exploiting competitive planner performance
Howe, A. E., Dahlman, E., Hansen, C., Scheetz, M., and von Mayrhauser, A. (1999) · 1999
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Popular ensemble methods: An empirical study
Opitz, D., and Maclin, R. (1999) · 1999
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How to get a free lunch (at no cost)
Rice, J. R., and Ramakrishnan, N. (1999) · 1999
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Evolution of constraint satisfaction strategies in examination timetabling
Terashima-Marín, H., Ross, P., and Valenzuela-Rendón, M. (1999) · 1999
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Dynamic problem structure analysis as a basis for constraint-directed scheduling heuristics
Beck, J. C., and Fox, M. S. (2000) · 2000
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A comparison of ranking methods for classification algorithm selection
Brazdil, P., and Soares, C. (2000) · 2000
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Ensemble methods in machine learning
Dietterich, T. G. (2000) · 2000
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Genetic algorithm portfolios
Fukunaga, A. S. (2000) · 2000
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Algorithm selection using reinforcement learning
Lagoudakis, M. G., and Littman, M. L. (2000) · 2000
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Meta-Learning by landmarking various learning algorithms
Pfahringer, B., Bensusan, H., and Giraud-Carrier, C. G. (2000) · 2000
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Improvements to and estimating the cost of solving constraint satisfaction problems
Sillito, J. (2000) · 2000
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Case-Based recommender components for scientific Problem-Solving environments
Wilson, D., Leake, D., and Bramley, R. (2000) · 2000
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A context for constraint satisfaction problem formulation selection
Borrett, J. E., and Tsang, E. P. K. (2001) · 2001
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Collaborative learning for constraint solving
Epstein, S. L., and Freuder, E. C. (2001) · 2001
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Algorithm portfolios
Gomes, C. P., and Selman, B. (2001) · 2001
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A bayesian approach to tackling hard computational problems
Horvitz, E., Ruan, Y., Gomes, C. P., Kautz, H. A., Selman, B., and Chickering, D. M. (2001) · 2001
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Learning to select branching rules in the DPLL procedure for satisfiability
Lagoudakis, M. G., and Littman, M. L. (2001) · 2001
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Choosing search heuristics by Non-Stationary reinforcement learning
Nareyek, A. (2001) · 2001
Cited alongside, same era.
The supervised learning No-Free-Lunch theorems
Wolpert, D. H. (2001) · 2001
Cited alongside, same era.
The adaptive constraint engine
Epstein, S. L., Freuder, E. C., Wallace, R., Morozov, A., and Samuels, B. (2002) · 2002
Cited alongside, same era.
Automated discovery of composite SAT variable-selection heuristics
Fukunaga, A. S. (2002) · 2002
Cited alongside, same era.
Learning the empirical hardness of optimization problems: The case of combinatorial auctions
Leyton-Brown, K., Nudelman, E., and Shoham, Y. (2002) · 2002
Cited alongside, same era.
Capturing constraint programming experience: A Case-Based approach
Little, J., Gebruers, C., Bridge, D., and Freuder, E. (2002) · 2002
Cited alongside, same era.
What makes planners predictable?
Roberts, M., Howe, A. E., Wilson, B., and desJardins, M. (2008) · 2008
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New techniques for algorithm portfolio design
Streeter, M. J., and Smith, S. F. (2008) · 2008
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Switching among Non-Weighting, clause weighting, and variable weighting in local search for SAT
Wei, W., Li, C. M., and Zhang, H. (2008) · 2008
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SATzilla: portfolio-based algorithm selection for SAT
Xu, L., Hutter, F., Hoos, H. H., and Leyton-Brown, K. (2008) · 2008
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PetaBricks: a language and compiler for algorithmic choice
Ansel, J., Chan, C., Wong, Y. L., Olszewski, M., Zhao, Q., Edelman, A., and Amarasinghe, S. (2009) · 2009
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A Gender-Based genetic algorithm for the automatic configuration of algorithms
Ansótegui, C., Sellmann, M., and Tierney, K. (2009) · 2009
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Case-Based reasoning as a heuristic selector in Hyper-Heuristic for course timetabling problems
Petrovic, S., and Qu, R. (2002) · 2002
Cited alongside, same era.
Algorithm Selection for Sorting and Probabilistic Inference: A Machine Learning-Based Approach
Guo, H. (2003) · 2003
Cited alongside, same era.
Learning rules for adaptive planning
Vrakas, D., Tsoumakas, G., Bassiliades, N., and Vlahavas, I. (2003) · 2003
Cited alongside, same era.
Empirical modeling and analysis of local search algorithms for the job-shop scheduling problem
Watson, J. (2003) · 2003
Cited alongside, same era.
Simple rules for low-knowledge algorithm selection
Beck, J. C., and Freuder, E. C. (2004) · 2004
Cited alongside, same era.
Low-Knowledge algorithm control
Carchrae, T., and Beck, J. C. (2004) · 2004
Cited alongside, same era.
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Online heuristic selection in constraint programming
Arbelaez, A., Hamadi, Y., and Sebag, M. (2009) · 2009
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Towards Low-Cost, High-Accuracy classifiers for linear solver selection
Bhowmick, S., Toth, B., and Raghavan, P. (2009) · 2009
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Combining multiple heuristics on discrete resources
Bougeret, M., Dutot, P., Goldman, A., Ngoko, Y., and Trystram, D. (2009) · 2009
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Low Knowledge Algorithm Control for Constraint-Based Scheduling
Carchrae, T. (2009) · 2009
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An automatically configurable portfolio-based planner with macro-actions: PbP
Gerevini, A. E., Saetti, A., and Vallati, M. (2009) · 2009
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Restart strategy selection using machine learning techniques
Haim, S., and Walsh, T. (2009) · 2009
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Automated Configuration of Algorithms for Solving Hard Computational Problems
Hutter, F. (2009) · 2009
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ParamILS: an automatic algorithm configuration framework
Hutter, F., Hoos, H. H., Leyton-Brown, K., and Stützle, T. (2009) · 2009
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Empirical hardness models: Methodology and a case study on combinatorial auctions
Leyton-Brown, K., Nudelman, E., and Shoham, Y. (2009) · 2009
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Instance-Based selection of policies for SAT solvers
Nikolić, M., Marić, F., and Janičić, P. (2009) · 2009
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A self-adaptive multi-engine solver for quantified boolean formulas
Pulina, L., and Tacchella, A. (2009) · 2009
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Learning how to propagate using random probing
Stamatatos, E., and Stergiou, K. (2009) · 2009
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Heuristics for dynamically adapting propagation in constraint satisfaction problems
Stergiou, K. (2009) · 2009
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SATzilla2009: an automatic algorithm portfolio for SAT
Xu, L., Hutter, F., Hoos, H. H., and Leyton-Brown, K. (2009) · 2009
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To max or not to max: Online learning for speeding up optimal planning
Domshlak, C., Karpas, E., and Markovitch, S. (2010) · 2010
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Synthesis of search algorithms from high-level CP models
Elsayed, S. A. M., and Michel, L. (2010) · 2010
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Automatic Algorithm Selection for Complex Simulation Problems
Ewald, R. (2010) · 2010
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Selecting simulation algorithm portfolios by genetic algorithms
Ewald, R., Schulz, R., and Uhrmacher, A. M. (2010) · 2010
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Online Dynamic Algorithm Portfolios – Minimizing the computational cost of problem solving
Gagliolo, M. (2010) · 2010
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DVRP: a hard dynamic combinatorial optimisation problem tackled by an evolutionary hyper-heuristic
Garrido, P., and Riff, M. (2010) · 2010
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ISAC – Instance-Specific algorithm configuration
Kadioglu, S., Malitsky, Y., Sellmann, M., and Tierney, K. (2010) · 2010
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Ensemble classification for constraint solver configuration
Kotthoff, L., Miguel, I., and Nightingale, P. (2010) · 2010
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Using active testing and Meta-Level information for selection of classification algorithms
Leite, R., Brazdil, P., Vanschoren, J., and Queiros, F. (2010) · 2010
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Latent class models for algorithm portfolio methods
Silverthorn, B., and Miikkulainen, R. (2010) · 2010
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Collaborative expert portfolio management
Stern, D. H., Samulowitz, H., Herbrich, R., Graepel, T., Pulina, L., and Tacchella, A. (2010) · 2010
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Hydra: Automatically configuring algorithms for Portfolio-Based selection
Xu, L., Hoos, H. H., and Leyton-Brown, K. (2010) · 2010
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Synthesis of search algorithms from high-level CP models
Elsayed, S. A. M., and Michel, L. (2011) · 2011
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Algorithm portfolio selection as a bandit problem with unbounded losses
Gagliolo, M., and Schmidhuber, J. (2011) · 2011
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A portfolio solver for answer set programming: preliminary report
Gebser, M., Kaminski, R., Kaufmann, B., Schaub, T., Schneider, M. T., and Ziller, S. (2011) · 2011
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Algorithm selection and scheduling
Kadioglu, S., Malitsky, Y., Sabharwal, A., Samulowitz, H., and Sellmann, M. (2011) · 2011
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Feature filtering for Instance-Specific algorithm configuration
Kroer, C., and Malitsky, Y. (2011) · 2011
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Non-model-based algorithm portfolios for SAT
Malitsky, Y., Sabharwal, A., Samulowitz, H., and Sellmann, M. (2011) · 2011
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Rational deployment of CSP heuristics
Tolpin, D., and Shimony, S. E. (2011) · 2011
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Hydra-MIP: automated algorithm configuration and selection for mixed integer programming
Xu, L., Hutter, F., Hoos, H. H., and Leyton-Brown, K. (2011) · 2011
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An automated approach to generating efficient constraint solvers
Balasubramaniam, D., Gent, I. P., Jefferson, C., Kotthoff, L., Miguel, I., and Nightingale, P. (2012) · 2012
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Programming by optimization
Hoos, H. H. (2012) · 2012
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Parallel algorithm configuration
Hutter, F., Hoos, H. H., and Leyton-Brown, K. (2012) · 2012
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An evaluation of machine learning in algorithm selection for search problems
Kotthoff, L., Gent, I. P., and Miguel, I. (2012) · 2012
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Instance-Specific Algorithm Configuration
Malitsky, Y. (2012) · 2012
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Measuring instance difficulty for combinatorial optimization problems
Smith-Miles, K., and Lopes, L. (2012) · 2012
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Evaluating component solver contributions to Portfolio-Based algorithm selectors
Xu, L., Hutter, F., Hoos, H. H., and Leyton-Brown, K. (2012) · 2012
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Learning algorithm portfolios for parallel execution
Yun, X., and Epstein, S. L. (2012) · 2012
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