Automatic algorithm selection for Pseudo-Boolean optimization with given computational time limits
Machine learning (ML) techniques have been proposed to automatically select the best solver from a portfolio of solvers. They have been applied to various problems including Boolean Satisfiability, Traveling Salesperson and Graph Coloring. These techniques are used to implement meta-solvers that rec...
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| Veröffentlicht in: | Computers & operations research Jg. 173; S. 106836 |
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| ISSN: | 0305-0548 |
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| Abstract | Machine learning (ML) techniques have been proposed to automatically select the best solver from a portfolio of solvers. They have been applied to various problems including Boolean Satisfiability, Traveling Salesperson and Graph Coloring. These techniques are used to implement meta-solvers that receive, as input, the instance of a problem, predict the best-performing solver in the portfolio, and execute it to deliver a solution. Typically, the quality of the solution improves with a longer computational time. This has led to the development of anytime meta-solvers, which consider both the instance and a user-prescribed computational time limit. Anytime meta-solvers predict the best-performing solver within the specified time limit. In this study, we focus on designing anytime meta-solvers for the NP-hard optimization problem of Pseudo-Boolean Optimization (PBO), which generalizes Satisfiability and Maximum Satisfiability problems. The effectiveness of our approach is demonstrated via extensive empirical study in which our anytime meta-solver, named PBO_MS, improves dramatically on the performance of Mixed Integer Programming solver Gurobi, which is the best-performing single solver in the portfolio. We generalize the anytime meta-solver by predicting a given number p≥1 of best solvers in the portfolio and then run these, each with equal share of the specified time limit. This anytime p-meta-solver is shown here to outperform both the anytime 1-meta-solver as well as a fixed selection of p solvers by a wide margin.
•First research on Algorithm Selection and Anytime Algorithm Selection for Pseudo Boolean Optimization (PBO).•Inclusion of a “no solution” special label to predict when a solution is not expected for a given time limit.•Inclusion of the mˆms for anytime scenarios.•Identification of new informative features for algorithm selection on PBO.•Implementation and comparison of dynamic portfolios for anytime PBO. |
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| AbstractList | Machine learning (ML) techniques have been proposed to automatically select the best solver from a portfolio of solvers. They have been applied to various problems including Boolean Satisfiability, Traveling Salesperson and Graph Coloring. These techniques are used to implement meta-solvers that receive, as input, the instance of a problem, predict the best-performing solver in the portfolio, and execute it to deliver a solution. Typically, the quality of the solution improves with a longer computational time. This has led to the development of anytime meta-solvers, which consider both the instance and a user-prescribed computational time limit. Anytime meta-solvers predict the best-performing solver within the specified time limit. In this study, we focus on designing anytime meta-solvers for the NP-hard optimization problem of Pseudo-Boolean Optimization (PBO), which generalizes Satisfiability and Maximum Satisfiability problems. The effectiveness of our approach is demonstrated via extensive empirical study in which our anytime meta-solver, named PBO_MS, improves dramatically on the performance of Mixed Integer Programming solver Gurobi, which is the best-performing single solver in the portfolio. We generalize the anytime meta-solver by predicting a given number p≥1 of best solvers in the portfolio and then run these, each with equal share of the specified time limit. This anytime p-meta-solver is shown here to outperform both the anytime 1-meta-solver as well as a fixed selection of p solvers by a wide margin.
•First research on Algorithm Selection and Anytime Algorithm Selection for Pseudo Boolean Optimization (PBO).•Inclusion of a “no solution” special label to predict when a solution is not expected for a given time limit.•Inclusion of the mˆms for anytime scenarios.•Identification of new informative features for algorithm selection on PBO.•Implementation and comparison of dynamic portfolios for anytime PBO. |
| ArticleNumber | 106836 |
| Author | Godoy, Julio Hochbaum, Dorit Pezo, Catalina Asín-Achá, Roberto |
| Author_xml | – sequence: 1 givenname: Catalina orcidid: 0000-0001-6828-1812 surname: Pezo fullname: Pezo, Catalina email: cpezov@berkeley.edu organization: Department of Industrial Engineering and Operations Research, University of California, Berkeley, USA – sequence: 2 givenname: Dorit orcidid: 0000-0002-2498-0512 surname: Hochbaum fullname: Hochbaum, Dorit email: dhochbaum@berkeley.edu organization: Department of Industrial Engineering and Operations Research, University of California, Berkeley, USA – sequence: 3 givenname: Julio orcidid: 0000-0002-4912-1837 surname: Godoy fullname: Godoy, Julio email: jugodoy@inf.udec.cl organization: Department of Computer Science, Universidad de Concepción, Chile – sequence: 4 givenname: Roberto surname: Asín-Achá fullname: Asín-Achá, Roberto email: roberto.asin@usm.cl organization: Department of Informatics, Universidad Técnica Federico Santa María, Chile |
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| Cites_doi | 10.1007/BF00116251 10.1016/S0004-3702(00)00081-3 10.1007/978-3-030-22475-2_1 10.1016/j.eswa.2020.113613 10.1016/j.cor.2021.105661 10.1016/j.artint.2016.04.003 10.1016/j.eswa.2021.115948 10.1016/S0065-2458(08)60520-3 10.1613/jair.1.13818 10.1016/j.cor.2021.105615 10.1007/s10479-012-1081-x 10.1007/BF00058655 10.1145/3065386 10.1613/jair.1.12228 10.1017/S1471068414000015 10.1017/S1471068413000094 10.1126/science.275.5296.51 10.1145/1858996.1859087 10.1587/transinf.2014FOP0007 10.1109/CVPR.2015.7298594 10.1023/A:1010933404324 10.1016/j.artint.2015.12.006 10.1016/S0166-218X(01)00341-9 10.1162/evco_a_00242 10.1016/j.cor.2013.11.015 10.1016/j.cor.2020.105184 10.1613/jair.2490 10.1016/j.artint.2018.10.004 |
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| References | Pulina, Tacchella (b45) 2007 Gomes, Selman (b23) 2001; 126 Smith-Miles, Christiansen, Muñoz (b51) 2021; 128 Kadioglu, Malitsky, Sellmann, Tierney (b30) 2010 Achá, López, Hagedorn, Baier (b1) 2022; 75 Martins, Manquinho, Lynce (b42) 2014 Gebser, Kaminski, Kaufmann, Schaub, Schneider, Ziller (b21) 2011 Biere, Heule, van Maaren (b8) 2009 Zhu, Goldberg (b59) 2009; 3 Quinlan (b46) 1986; 1 Krizhevsky, Sutskever, Hinton (b33) 2017; 60 Loreggia, Malitsky, Samulowitz, Saraswat (b36) 2016 Wolsey, Nemhauser (b57) 1999 Bischl, Kerschke, Kotthoff, Lindauer, Malitsky, Fréchette, Hoos, Hutter, Leyton-Brown, Tierney, Vanschoren (b10) 2016 Kerschke, Hoos, Neumann, Trautmann (b31) 2019; 27 Breiman (b14) 2017 Asín Achá, Nieuwenhuis (b7) 2014; 218 Amadini, Stuckey (b4) 2014 Guo, Zhen, Li, Luo, Yuan, Jin, Yan (b24) 2023 Boros, Hammer (b11) 2002; 123 Alloghani, Al-Jumeily, Mustafina, Hussain, Aljaaf (b2) 2020 Burkart, Huber (b15) 2021; 70 Ansótegui, Bonet, Gabas, Levy (b5) 2012 Breiman (b13) 2001; 45 Fix, Hodges (b19) 1991 Friedman (b20) 2001 Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A., 2015. Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1–9. Breiman (b12) 1996; 24 Nudelman, Leyton-Brown, Devkar, Shoham, Hoos (b43) 2004; 2004 Wille, Zhang, Drechsler (b56) 2011 Gurobi Optimization, LLC (b25) 2023 Pedregosa, Varoquaux, Gramfort, Michel, Thirion, Grisel, Blondel, Prettenhofer, Weiss, Dubourg, Vanderplas, Passos, Cournapeau, Brucher, Perrot, Duchesnay (b44) 2011; 12 De Souza, Ritt, López-Ibáñez (b17) 2022; 139 Strassl, Musliu (b53) 2022; 141 Martins, Joshi, Manquinho, Lynce (b41) 2014 Huerta, Neira, Ortega, Varas, Godoy, Asín-Achá (b28) 2020; 158 Ansótegui, Gabas, Malitsky, Sellmann (b6) 2016; 235 Manquinho, Marques-Silva (b38) 2005 Lei, Cai, Luo, Hoos (b34) 2021 Hoos, Kaminski, Lindauer, Schaub (b26) 2015; 15 Trezentos, P., Lynce, I., Oliveira, A.L., 2010. Apt-pbo: solving the software dependency problem using pseudo-boolean optimization. In: Proceedings of the IEEE/ACM International Conference on Automated Software Engineering. pp. 427–436. Alpaydin (b3) 2021 Maratea, Pulina, Ricca (b40) 2014; 14 Biere, Heule, van Maaren, Walsh (b9) 2009 Smith-Miles, Baatar, Wreford, Lewis (b50) 2014; 45 Sakai, Nabeshima (b48) 2015; 98 Huerta, Neira, Ortega, Varas, Godoy, Asín-Achá (b29) 2022; 187 Manquinho, Roussel, Deters (b39) 2011 Huberman, Lukose, Hogg (b27) 1997; 275 Simonyan, Zisserman (b49) 2014 Xu, Hutter, Hoos, Leyton-Brown (b58) 2008; 32 Covarrubias (b16) 2016 Koshimura, Zhang, Fujita, Hasegawa (b32) 2012; 8 Malitsky, Sabharwal, Samulowitz, Sellmann (b37) 2012 Gebser, Kaufmann, Neumann, Schaub (b22) 2007 Sörensson (b52) 2010; 2010 Rice (b47) 1976; vol. 15 Lindauer, van Rijn, Kotthoff (b35) 2019; 272 Elffers, Nordström (b18) 2018; 18 Asín Achá (10.1016/j.cor.2024.106836_b7) 2014; 218 Amadini (10.1016/j.cor.2024.106836_b4) 2014 Biere (10.1016/j.cor.2024.106836_b9) 2009 Maratea (10.1016/j.cor.2024.106836_b40) 2014; 14 Nudelman (10.1016/j.cor.2024.106836_b43) 2004; 2004 Bischl (10.1016/j.cor.2024.106836_b10) 2016 Wille (10.1016/j.cor.2024.106836_b56) 2011 Gebser (10.1016/j.cor.2024.106836_b21) 2011 Breiman (10.1016/j.cor.2024.106836_b14) 2017 Huerta (10.1016/j.cor.2024.106836_b29) 2022; 187 Strassl (10.1016/j.cor.2024.106836_b53) 2022; 141 Zhu (10.1016/j.cor.2024.106836_b59) 2009; 3 Breiman (10.1016/j.cor.2024.106836_b12) 1996; 24 Friedman (10.1016/j.cor.2024.106836_b20) 2001 Kerschke (10.1016/j.cor.2024.106836_b31) 2019; 27 Pedregosa (10.1016/j.cor.2024.106836_b44) 2011; 12 Lei (10.1016/j.cor.2024.106836_b34) 2021 Guo (10.1016/j.cor.2024.106836_b24) 2023 Malitsky (10.1016/j.cor.2024.106836_b37) 2012 Smith-Miles (10.1016/j.cor.2024.106836_b51) 2021; 128 Kadioglu (10.1016/j.cor.2024.106836_b30) 2010 Wolsey (10.1016/j.cor.2024.106836_b57) 1999 Krizhevsky (10.1016/j.cor.2024.106836_b33) 2017; 60 Koshimura (10.1016/j.cor.2024.106836_b32) 2012; 8 Simonyan (10.1016/j.cor.2024.106836_b49) 2014 10.1016/j.cor.2024.106836_b54 10.1016/j.cor.2024.106836_b55 Ansótegui (10.1016/j.cor.2024.106836_b6) 2016; 235 Gurobi Optimization, LLC (10.1016/j.cor.2024.106836_b25) 2023 Martins (10.1016/j.cor.2024.106836_b41) 2014 Alpaydin (10.1016/j.cor.2024.106836_b3) 2021 Xu (10.1016/j.cor.2024.106836_b58) 2008; 32 Quinlan (10.1016/j.cor.2024.106836_b46) 1986; 1 Ansótegui (10.1016/j.cor.2024.106836_b5) 2012 Manquinho (10.1016/j.cor.2024.106836_b39) 2011 Breiman (10.1016/j.cor.2024.106836_b13) 2001; 45 Burkart (10.1016/j.cor.2024.106836_b15) 2021; 70 Manquinho (10.1016/j.cor.2024.106836_b38) 2005 Loreggia (10.1016/j.cor.2024.106836_b36) 2016 Biere (10.1016/j.cor.2024.106836_b8) 2009 Hoos (10.1016/j.cor.2024.106836_b26) 2015; 15 Huberman (10.1016/j.cor.2024.106836_b27) 1997; 275 Fix (10.1016/j.cor.2024.106836_b19) 1991 Pulina (10.1016/j.cor.2024.106836_b45) 2007 Boros (10.1016/j.cor.2024.106836_b11) 2002; 123 Covarrubias (10.1016/j.cor.2024.106836_b16) 2016 Sakai (10.1016/j.cor.2024.106836_b48) 2015; 98 Rice (10.1016/j.cor.2024.106836_b47) 1976; vol. 15 Sörensson (10.1016/j.cor.2024.106836_b52) 2010; 2010 Lindauer (10.1016/j.cor.2024.106836_b35) 2019; 272 Martins (10.1016/j.cor.2024.106836_b42) 2014 Achá (10.1016/j.cor.2024.106836_b1) 2022; 75 Huerta (10.1016/j.cor.2024.106836_b28) 2020; 158 Alloghani (10.1016/j.cor.2024.106836_b2) 2020 Gebser (10.1016/j.cor.2024.106836_b22) 2007 Gomes (10.1016/j.cor.2024.106836_b23) 2001; 126 De Souza (10.1016/j.cor.2024.106836_b17) 2022; 139 Elffers (10.1016/j.cor.2024.106836_b18) 2018; 18 Smith-Miles (10.1016/j.cor.2024.106836_b50) 2014; 45 |
| References_xml | – start-page: 332 year: 2021 end-page: 348 ident: b34 article-title: Efficient local search for pseudo boolean optimization publication-title: Theory and Applications of Satisfiability Testing–SAT 2021: 24th International Conference, Barcelona, Spain, July 5-9, 2021, Proceedings 24 – start-page: 531 year: 2014 end-page: 548 ident: b41 article-title: Incremental cardinality constraints for maxsat publication-title: International Conference on Principles and Practice of Constraint Programming – start-page: 260 year: 2007 end-page: 265 ident: b22 article-title: Clasp: A conflict-driven answer set solver publication-title: International Conference on Logic Programming and Nonmonotonic Reasoning – volume: 60 start-page: 84 year: 2017 end-page: 90 ident: b33 article-title: Imagenet classification with deep convolutional neural networks publication-title: Commun. ACM – start-page: 32 year: 1991 end-page: 39 ident: b19 article-title: Discriminatory analysis: nonparametric discrimination: consistency properties publication-title: Nearest Neighbor (NN) Norms NN Pattern Classif. Tech – start-page: 352 year: 2011 end-page: 357 ident: b21 article-title: A portfolio solver for answer set programming: Preliminary report publication-title: Logic Programming and Nonmonotonic Reasoning: 11th International Conference, LPNMR 2011, Vancouver, Canada, May 16-19, 2011. Proceedings 11 – start-page: 41 year: 2016 end-page: 58 ident: b10 article-title: ASlib: A Benchmark Library for Algorithm Selection publication-title: Artificial Intelligence J. (AIJ) – volume: 187 year: 2022 ident: b29 article-title: Improving the state-of-the-art in the traveling salesman problem: An anytime automatic algorithm selection publication-title: Expert Syst. Appl. – volume: 235 start-page: 26 year: 2016 end-page: 39 ident: b6 article-title: Maxsat by improved instance-specific algorithm configuration publication-title: Artificial Intelligence – start-page: 120 year: 2011 end-page: 125 ident: b56 article-title: ATPG for reversible circuits using simulation, boolean satisfiability, and pseudo boolean optimization publication-title: 2011 IEEE Computer Society Annual Symposium on VLSI – volume: 2010 year: 2010 ident: b52 article-title: Minisat 2.2 and minisat++ 1.1 publication-title: A Short Descr. SAT Race – year: 2009 ident: b8 article-title: Handbook of satisfiability – volume: 98 start-page: 1121 year: 2015 end-page: 1127 ident: b48 article-title: Construction of an ROBDD for a PB-constraint in band form and related techniques for PB-solvers publication-title: IEICE Trans. Inf. Syst. – start-page: 131 year: 2009 end-page: 153 ident: b9 article-title: Conflict-driven clause learning sat solvers publication-title: Handb. Satisf. Front. Artif. Intell. Appl. – volume: 2004 year: 2004 ident: b43 article-title: Satzilla: An algorithm portfolio for SAT publication-title: Solv. Descr. SAT compet. – year: 2023 ident: b25 article-title: Gurobi optimizer reference manual – volume: 218 start-page: 71 year: 2014 end-page: 91 ident: b7 article-title: Curriculum-based course timetabling with SAT and maxsat publication-title: Ann. Oper. Res. – volume: 275 start-page: 51 year: 1997 end-page: 54 ident: b27 article-title: An economics approach to hard computational problems publication-title: Science – volume: 15 start-page: 117 year: 2015 end-page: 142 ident: b26 article-title: Aspeed: Solver scheduling via answer set programming1 publication-title: Theory Pract. Log. Program. – volume: 139 year: 2022 ident: b17 article-title: Capping methods for the automatic configuration of optimization algorithms publication-title: Comput. Oper. Res. – year: 2011 ident: b39 article-title: Pseudo-boolean competition 2010 – volume: 70 start-page: 245 year: 2021 end-page: 317 ident: b15 article-title: A survey on the explainability of supervised machine learning publication-title: J. Artificial Intelligence Res. – volume: 18 start-page: 1291 year: 2018 end-page: 1299 ident: b18 article-title: Divide and conquer: Towards faster pseudo-boolean solving. publication-title: IJCAI – volume: 126 start-page: 43 year: 2001 end-page: 62 ident: b23 article-title: Algorithm portfolios publication-title: Artificial Intelligence – start-page: 1189 year: 2001 end-page: 1232 ident: b20 article-title: Greedy function approximation: a gradient boosting machine publication-title: Ann. Statist. – volume: 45 start-page: 12 year: 2014 end-page: 24 ident: b50 article-title: Towards objective measures of algorithm performance across instance space publication-title: Comput. Oper. Res. – start-page: 660 year: 2005 end-page: 665 ident: b38 article-title: Effective lower bounding techniques for pseudo-boolean optimization [eda applications] publication-title: Design, Automation and Test in Europe – volume: 32 start-page: 565 year: 2008 end-page: 606 ident: b58 article-title: Satzilla: portfolio-based algorithm selection for SAT publication-title: J. Artificial Intelligence Res. – year: 2021 ident: b3 article-title: Machine learning – volume: 8 start-page: 95 year: 2012 end-page: 100 ident: b32 article-title: Qmaxsat: A partial max-SAT solver publication-title: J. Satisf. Boolean Model. Comput. – start-page: 512 year: 2012 end-page: 526 ident: b37 article-title: Parallel SAT solver selection and scheduling publication-title: Principles and Practice of Constraint Programming: 18th International Conference, CP 2012, QuÉBec City, QC, Canada, October 8-12, 2012. Proceedings – volume: 75 start-page: 323 year: 2022 end-page: 350 ident: b1 article-title: Multi-agent path finding: A new boolean encoding publication-title: J. Artificial Intelligence Res. – year: 2016 ident: b16 article-title: Portfolio approaches for problem solving – start-page: 1 year: 2023 end-page: 16 ident: b24 article-title: Machine learning methods in solving the boolean satisfiability problem publication-title: Mach. Intell. Res. – reference: Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A., 2015. Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1–9. – volume: 123 start-page: 155 year: 2002 end-page: 225 ident: b11 article-title: Pseudo-boolean optimization publication-title: Discrete Appl. Math. – volume: 272 start-page: 86 year: 2019 end-page: 100 ident: b35 article-title: The algorithm selection competitions 2015 and 2017 publication-title: Artificial Intelligence – start-page: 438 year: 2014 end-page: 445 ident: b42 article-title: Open-WBO: A modular maxsat solver publication-title: International Conference on Theory and Applications of Satisfiability Testing – reference: Trezentos, P., Lynce, I., Oliveira, A.L., 2010. Apt-pbo: solving the software dependency problem using pseudo-boolean optimization. In: Proceedings of the IEEE/ACM International Conference on Automated Software Engineering. pp. 427–436. – start-page: 751 year: 2010 end-page: 756 ident: b30 article-title: ISAC–instance-specific algorithm configuration publication-title: ECAI 2010 – start-page: 86 year: 2012 end-page: 101 ident: b5 article-title: Improving SAT-based weighted maxsat solvers publication-title: International Conference on Principles and Practice of Constraint Programming – year: 2016 ident: b36 article-title: Deep learning for algorithm portfolios publication-title: Thirtieth AAAI Conference on Artificial Intelligence – volume: 12 start-page: 2825 year: 2011 end-page: 2830 ident: b44 article-title: Scikit-learn: Machine learning in python publication-title: J. Mach. Learn. Res. – volume: 14 start-page: 841 year: 2014 end-page: 868 ident: b40 article-title: A multi-engine approach to answer-set programming publication-title: Theory Pract. Log. Program. – start-page: 108 year: 2014 end-page: 124 ident: b4 article-title: Sequential time splitting and bounds communication for a portfolio of optimization solvers publication-title: Principles and Practice of Constraint Programming: 20th International Conference, CP 2014, Lyon, France, September 8-12, 2014. Proceedings 20 – volume: 141 year: 2022 ident: b53 article-title: Instance space analysis and algorithm selection for the job shop scheduling problem publication-title: Comput. Oper. Res. – start-page: 574 year: 2007 end-page: 589 ident: b45 article-title: A multi-engine solver for quantified boolean formulas publication-title: Principles and Practice of Constraint Programming–CP 2007: 13th International Conference, CP 2007, Providence, RI, USA, September 23-27, 2007. Proceedings 13 – volume: 158 year: 2020 ident: b28 article-title: Anytime automatic algorithm selection for knapsack publication-title: Expert Syst. Appl. – year: 2014 ident: b49 article-title: Very deep convolutional networks for large-scale image recognition – year: 1999 ident: b57 article-title: Integer and combinatorial optimization – volume: vol. 15 start-page: 65 year: 1976 end-page: 118 ident: b47 article-title: The algorithm selection problem publication-title: Advances in Computers – start-page: 3 year: 2020 end-page: 21 ident: b2 article-title: A systematic review on supervised and unsupervised machine learning algorithms for data science publication-title: Superv. Unsupervised Learn. Data Sci. – volume: 128 year: 2021 ident: b51 article-title: Revisiting where are the hard knapsack problems? via instance space analysis publication-title: Comput. Oper. Res. – volume: 27 start-page: 3 year: 2019 end-page: 45 ident: b31 article-title: Automated algorithm selection: Survey and perspectives publication-title: Evol. Comput. – volume: 24 start-page: 123 year: 1996 end-page: 140 ident: b12 article-title: Bagging predictors publication-title: Mach. Learn. – volume: 45 start-page: 5 year: 2001 end-page: 32 ident: b13 article-title: Random forests publication-title: Mach. Learn. – year: 2017 ident: b14 article-title: Classification and regression trees – volume: 1 start-page: 81 year: 1986 end-page: 106 ident: b46 article-title: Induction of decision trees publication-title: Mach. Learn. – volume: 3 start-page: 1 year: 2009 end-page: 130 ident: b59 article-title: Introduction to semi-supervised learning publication-title: Synth. Lect. Artif. Intell. Mach. Learn. – volume: 1 start-page: 81 issue: 1 year: 1986 ident: 10.1016/j.cor.2024.106836_b46 article-title: Induction of decision trees publication-title: Mach. Learn. doi: 10.1007/BF00116251 – year: 2016 ident: 10.1016/j.cor.2024.106836_b36 article-title: Deep learning for algorithm portfolios – year: 2017 ident: 10.1016/j.cor.2024.106836_b14 – volume: 126 start-page: 43 issue: 1–2 year: 2001 ident: 10.1016/j.cor.2024.106836_b23 article-title: Algorithm portfolios publication-title: Artificial Intelligence doi: 10.1016/S0004-3702(00)00081-3 – volume: 2010 year: 2010 ident: 10.1016/j.cor.2024.106836_b52 article-title: Minisat 2.2 and minisat++ 1.1 publication-title: A Short Descr. SAT Race – start-page: 3 year: 2020 ident: 10.1016/j.cor.2024.106836_b2 article-title: A systematic review on supervised and unsupervised machine learning algorithms for data science publication-title: Superv. Unsupervised Learn. Data Sci. doi: 10.1007/978-3-030-22475-2_1 – volume: 158 year: 2020 ident: 10.1016/j.cor.2024.106836_b28 article-title: Anytime automatic algorithm selection for knapsack publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2020.113613 – start-page: 120 year: 2011 ident: 10.1016/j.cor.2024.106836_b56 article-title: ATPG for reversible circuits using simulation, boolean satisfiability, and pseudo boolean optimization – year: 1999 ident: 10.1016/j.cor.2024.106836_b57 – start-page: 660 year: 2005 ident: 10.1016/j.cor.2024.106836_b38 article-title: Effective lower bounding techniques for pseudo-boolean optimization [eda applications] – year: 2023 ident: 10.1016/j.cor.2024.106836_b25 – start-page: 438 year: 2014 ident: 10.1016/j.cor.2024.106836_b42 article-title: Open-WBO: A modular maxsat solver – volume: 141 year: 2022 ident: 10.1016/j.cor.2024.106836_b53 article-title: Instance space analysis and algorithm selection for the job shop scheduling problem publication-title: Comput. Oper. Res. doi: 10.1016/j.cor.2021.105661 – start-page: 41 issue: 237 year: 2016 ident: 10.1016/j.cor.2024.106836_b10 article-title: ASlib: A Benchmark Library for Algorithm Selection publication-title: Artificial Intelligence J. (AIJ) doi: 10.1016/j.artint.2016.04.003 – volume: 187 year: 2022 ident: 10.1016/j.cor.2024.106836_b29 article-title: Improving the state-of-the-art in the traveling salesman problem: An anytime automatic algorithm selection publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2021.115948 – volume: vol. 15 start-page: 65 year: 1976 ident: 10.1016/j.cor.2024.106836_b47 article-title: The algorithm selection problem doi: 10.1016/S0065-2458(08)60520-3 – start-page: 531 year: 2014 ident: 10.1016/j.cor.2024.106836_b41 article-title: Incremental cardinality constraints for maxsat – volume: 75 start-page: 323 year: 2022 ident: 10.1016/j.cor.2024.106836_b1 article-title: Multi-agent path finding: A new boolean encoding publication-title: J. Artificial Intelligence Res. doi: 10.1613/jair.1.13818 – volume: 139 year: 2022 ident: 10.1016/j.cor.2024.106836_b17 article-title: Capping methods for the automatic configuration of optimization algorithms publication-title: Comput. Oper. Res. doi: 10.1016/j.cor.2021.105615 – volume: 3 start-page: 1 issue: 1 year: 2009 ident: 10.1016/j.cor.2024.106836_b59 article-title: Introduction to semi-supervised learning publication-title: Synth. Lect. Artif. Intell. Mach. Learn. – volume: 218 start-page: 71 issue: 1 year: 2014 ident: 10.1016/j.cor.2024.106836_b7 article-title: Curriculum-based course timetabling with SAT and maxsat publication-title: Ann. Oper. Res. doi: 10.1007/s10479-012-1081-x – volume: 24 start-page: 123 issue: 2 year: 1996 ident: 10.1016/j.cor.2024.106836_b12 article-title: Bagging predictors publication-title: Mach. Learn. doi: 10.1007/BF00058655 – volume: 60 start-page: 84 issue: 6 year: 2017 ident: 10.1016/j.cor.2024.106836_b33 article-title: Imagenet classification with deep convolutional neural networks publication-title: Commun. ACM doi: 10.1145/3065386 – start-page: 32 year: 1991 ident: 10.1016/j.cor.2024.106836_b19 article-title: Discriminatory analysis: nonparametric discrimination: consistency properties publication-title: Nearest Neighbor (NN) Norms NN Pattern Classif. Tech – start-page: 86 year: 2012 ident: 10.1016/j.cor.2024.106836_b5 article-title: Improving SAT-based weighted maxsat solvers – volume: 70 start-page: 245 year: 2021 ident: 10.1016/j.cor.2024.106836_b15 article-title: A survey on the explainability of supervised machine learning publication-title: J. Artificial Intelligence Res. doi: 10.1613/jair.1.12228 – volume: 18 start-page: 1291 year: 2018 ident: 10.1016/j.cor.2024.106836_b18 article-title: Divide and conquer: Towards faster pseudo-boolean solving. – start-page: 332 year: 2021 ident: 10.1016/j.cor.2024.106836_b34 article-title: Efficient local search for pseudo boolean optimization – volume: 12 start-page: 2825 year: 2011 ident: 10.1016/j.cor.2024.106836_b44 article-title: Scikit-learn: Machine learning in python publication-title: J. Mach. Learn. Res. – start-page: 751 year: 2010 ident: 10.1016/j.cor.2024.106836_b30 article-title: ISAC–instance-specific algorithm configuration – volume: 15 start-page: 117 issue: 1 year: 2015 ident: 10.1016/j.cor.2024.106836_b26 article-title: Aspeed: Solver scheduling via answer set programming1 publication-title: Theory Pract. Log. Program. doi: 10.1017/S1471068414000015 – start-page: 1189 year: 2001 ident: 10.1016/j.cor.2024.106836_b20 article-title: Greedy function approximation: a gradient boosting machine publication-title: Ann. Statist. – start-page: 260 year: 2007 ident: 10.1016/j.cor.2024.106836_b22 article-title: Clasp: A conflict-driven answer set solver – volume: 14 start-page: 841 issue: 6 year: 2014 ident: 10.1016/j.cor.2024.106836_b40 article-title: A multi-engine approach to answer-set programming publication-title: Theory Pract. Log. Program. doi: 10.1017/S1471068413000094 – volume: 8 start-page: 95 issue: 1–2 year: 2012 ident: 10.1016/j.cor.2024.106836_b32 article-title: Qmaxsat: A partial max-SAT solver publication-title: J. Satisf. Boolean Model. Comput. – volume: 275 start-page: 51 issue: 5296 year: 1997 ident: 10.1016/j.cor.2024.106836_b27 article-title: An economics approach to hard computational problems publication-title: Science doi: 10.1126/science.275.5296.51 – ident: 10.1016/j.cor.2024.106836_b55 doi: 10.1145/1858996.1859087 – volume: 98 start-page: 1121 issue: 6 year: 2015 ident: 10.1016/j.cor.2024.106836_b48 article-title: Construction of an ROBDD for a PB-constraint in band form and related techniques for PB-solvers publication-title: IEICE Trans. Inf. Syst. doi: 10.1587/transinf.2014FOP0007 – start-page: 108 year: 2014 ident: 10.1016/j.cor.2024.106836_b4 article-title: Sequential time splitting and bounds communication for a portfolio of optimization solvers – start-page: 1 year: 2023 ident: 10.1016/j.cor.2024.106836_b24 article-title: Machine learning methods in solving the boolean satisfiability problem publication-title: Mach. Intell. Res. – ident: 10.1016/j.cor.2024.106836_b54 doi: 10.1109/CVPR.2015.7298594 – year: 2014 ident: 10.1016/j.cor.2024.106836_b49 – volume: 45 start-page: 5 issue: 1 year: 2001 ident: 10.1016/j.cor.2024.106836_b13 article-title: Random forests publication-title: Mach. Learn. doi: 10.1023/A:1010933404324 – volume: 2004 year: 2004 ident: 10.1016/j.cor.2024.106836_b43 article-title: Satzilla: An algorithm portfolio for SAT publication-title: Solv. Descr. SAT compet. – year: 2016 ident: 10.1016/j.cor.2024.106836_b16 – start-page: 512 year: 2012 ident: 10.1016/j.cor.2024.106836_b37 article-title: Parallel SAT solver selection and scheduling – volume: 235 start-page: 26 year: 2016 ident: 10.1016/j.cor.2024.106836_b6 article-title: Maxsat by improved instance-specific algorithm configuration publication-title: Artificial Intelligence doi: 10.1016/j.artint.2015.12.006 – volume: 123 start-page: 155 issue: 1–3 year: 2002 ident: 10.1016/j.cor.2024.106836_b11 article-title: Pseudo-boolean optimization publication-title: Discrete Appl. Math. doi: 10.1016/S0166-218X(01)00341-9 – start-page: 131 year: 2009 ident: 10.1016/j.cor.2024.106836_b9 article-title: Conflict-driven clause learning sat solvers publication-title: Handb. Satisf. Front. Artif. Intell. Appl. – volume: 27 start-page: 3 issue: 1 year: 2019 ident: 10.1016/j.cor.2024.106836_b31 article-title: Automated algorithm selection: Survey and perspectives publication-title: Evol. Comput. doi: 10.1162/evco_a_00242 – start-page: 352 year: 2011 ident: 10.1016/j.cor.2024.106836_b21 article-title: A portfolio solver for answer set programming: Preliminary report – year: 2011 ident: 10.1016/j.cor.2024.106836_b39 – year: 2009 ident: 10.1016/j.cor.2024.106836_b8 – start-page: 574 year: 2007 ident: 10.1016/j.cor.2024.106836_b45 article-title: A multi-engine solver for quantified boolean formulas – volume: 45 start-page: 12 year: 2014 ident: 10.1016/j.cor.2024.106836_b50 article-title: Towards objective measures of algorithm performance across instance space publication-title: Comput. Oper. Res. doi: 10.1016/j.cor.2013.11.015 – year: 2021 ident: 10.1016/j.cor.2024.106836_b3 – volume: 128 year: 2021 ident: 10.1016/j.cor.2024.106836_b51 article-title: Revisiting where are the hard knapsack problems? via instance space analysis publication-title: Comput. Oper. Res. doi: 10.1016/j.cor.2020.105184 – volume: 32 start-page: 565 year: 2008 ident: 10.1016/j.cor.2024.106836_b58 article-title: Satzilla: portfolio-based algorithm selection for SAT publication-title: J. Artificial Intelligence Res. doi: 10.1613/jair.2490 – volume: 272 start-page: 86 year: 2019 ident: 10.1016/j.cor.2024.106836_b35 article-title: The algorithm selection competitions 2015 and 2017 publication-title: Artificial Intelligence doi: 10.1016/j.artint.2018.10.004 |
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