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ISAC for Algorithm Selection

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Instance-Specific Algorithm Configuration

Abstract

The inception of algorithm portfolios has had a dramatic impact on constraint programming, operations research, and many other fields. Based on the observation that solvers have complementary strengths and therefore exhibit incomparable behavior on different problem instances, the ideas of running multiple solvers in parallel or selecting one solver based on the features of a given instance were introduced. Appropriately, these approaches have been named algorithm portfolios. Portfolio research has led to a wealth of different approaches and an amazing boost in solver performance in the past decade. This chapter demonstrates how the ISAC methodology can be applied to this task. Ultimately, here we aim to develop algorithm portfolios that are able to deal effectively with a vast range of input instances from a variety of sources.

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Notes

  1. 1.

    Note that the benchmark for a portfolio generator consists of both the training and test sets of problem instances as well as the solvers used to build the portfolio!

  2. 2.

    Our thanks go to Bryan Silverthorn, who provided the 610 instances used in the experiments in [103], as well as the runtime of the constituent solvers on his hardware, and also the final schedule of solvers that the latent class model found (see [103] for details).

  3. 3.

    Information that was generously provided by Mladen Nikolic

  4. 4.

    We are grateful to Lin Xu, who provided the Hydra-tuned SATensteins as well as the mapping of test instances to solvers.

References

  1. SAT Competition. http://www.satcomptition.org, 2013

  2. H. Abdi, L.J. Williams, Principal component analysis. Wiley Interdiscip. Rev. Comput. Stat. 2, 433–459 (2010)

    Article  Google Scholar 

  3. T. Achterberg, T. Koch, A. Martin, Branching rules revisited. Oper. Res. Lett. 33(1), 42–54 (2004)

    Article  MathSciNet  Google Scholar 

  4. B. Adenso-Diaz, M. Laguna, Fine-tuning of algorithms using fractional experimental designs and local search. Oper. Res. 54(1), 99–114 (2006)

    Article  MATH  Google Scholar 

  5. C. Ansótegui, M. Sellmann, K. Tierney, A gender-based genetic algorithm for the automatic configuration of algorithms, in Proceedings of Principles and Practice of Constraint Programming (CP), vol. 5732, ed. by I. Gent (Springer, Berlin, 2009), pp. 142–157

    Google Scholar 

  6. A. Atamtürk, G.L. Nemhauser, M.W.P. Savelsbergh, Valid inequalities for problems with additive variable upper bounds. Math. Program. 91(1), 145–162 (2001)

    MATH  MathSciNet  Google Scholar 

  7. A. Atamtürk, Flow pack facets of the single node fixed-charge flow polytope. Oper. Res. Lett. 29(3), 107–114 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  8. A. Atamtürk, On the facets of the mixed-integer knapsack polyhedron. Math. Program. 98(1–3), 145–175 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  9. A. Atamtürk, J.C. Munoz, A study of the lot-sizing polytope. Math. Program. 99(3), 443–465 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  10. G. Audemard, L. Simon, GLUCOSE: a solver that predicts learnt clauses quality. SAT Competition (2009)

    Google Scholar 

  11. C. Audet, D. Orban, Finding optimal algorithmic parameters using derivative-free optimization. SIAM J. Optim. 17(3), 642–664 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  12. A. Balint, M. Henn, O. Gableske, hybridGM. Solver description. SAT Competition (2009)

    Google Scholar 

  13. R. Battiti, G. Tecchiolli, I. Nazionale, F. Nucleare, The reactive tabu search. INFORMS J. Comput. 6(2), 126–140 (1993)

    Article  Google Scholar 

  14. P. Berkhin, Survey of clustering data mining techniques, in Grouping Multidimensional Data, ed. by J. Kogan, C. Nicholas, M. Teboulle (Springer, Berlin, Heidelberg, 2002), pp. 25–71

    Google Scholar 

  15. A. Biere, Picosat version 846. Solver description. SAT Competition (2007)

    Google Scholar 

  16. A. Biere, P{re,i}coSATSC’09. SAT Competition (2009)

    Google Scholar 

  17. A. Biere, Lingeling. SAT Race (2010)

    Google Scholar 

  18. A. Biere, PLingeling. SAT Race (2010)

    Google Scholar 

  19. A. Biere, Lingeling and Friends at the SAT Competition 2011. Technical report, 4040 Linz (2011)

    Google Scholar 

  20. M. Birattari, T. Stützle, L. Paquete, K. Varrentrapp, A racing algorithm for configuring metaheuristics, in Proceedings of the Genetic and Evolutionary Computation Conference (Morgan Kaufmann publishers, San Francisco, 2002), pp. 11–18

    Google Scholar 

  21. J. Boyan, A.W. Moore, P. Kaelbling, Learning evaluation functions to improve optimization by local search. J. Mach. Learn. Res. 1, 77–112 (2000)

    Google Scholar 

  22. A. Braunstein, M. Mézard, R. Zecchina, Survey propagation: an algorithm for Satisfiability. Random Struct. Algorithm. 27(2), 201–226 (2005)

    Article  MATH  Google Scholar 

  23. D.R. Bregman, The SAT Solver MXC, Version 0.99. SAT Competition (2009)

    Google Scholar 

  24. D.R. Bregman, D.G. Mitchell, The SAT Solver MXC, version 0.75. Solver description. SAT Race (2008)

    Google Scholar 

  25. L. Breiman, Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)

    MATH  MathSciNet  Google Scholar 

  26. L. Breiman, Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  MATH  Google Scholar 

  27. S. Cai, K. Su, Configuration checking with aspiration in local search for SAT, in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) (AAAI Press, Toronto, 2012), pp. 434–440

    Google Scholar 

  28. A. Caprara, M. Fischetti, P. Toth, D. Vigo, P.L. Guida, Algorithms for railway crew management. Math. Program. 79, 125–141 (1997)

    MATH  MathSciNet  Google Scholar 

  29. S.P. Coy, B.L. Golden, G.C. Runger, E.A. Wasil, Using experimental design to find effective parameter settings for heuristics. J. Heuristics 7(1), 77–97 (2001)

    Article  MATH  Google Scholar 

  30. G.B. Dantzig, P. Wolfe, The decomposition algorithm for linear programs. Econometrica 29(4), 767–778 (1961)

    Article  MATH  MathSciNet  Google Scholar 

  31. M. Davis, G. Logemann, D. Loveland, A machine program for theorem-proving. Commun. ACM 5(7), 394–397 (1962)

    Article  MATH  MathSciNet  Google Scholar 

  32. G. Dequen, O. Dubois, kcnfs. Solver description. SAT Competition (2007)

    Google Scholar 

  33. N. Een, N. Sörensson, MiniSAT (2010). http://minisat.se

  34. S.L. Epstein, E.C. Freuder, R.J. Wallace, A. Morozov, B. Samuels, The adaptive constraint engine, in Principles and Practice of Constraint Programming (CP), vol. 2470, ed. by P.V. Hentenryck (Springer, Berlin, Heidelberg, 2002), pp. 525–542

    Google Scholar 

  35. A.S. Fukunaga, Automated discovery of local search heuristics for satisfiability testing. Evol. Comput. 16(1), 31–61 (2008)

    Article  Google Scholar 

  36. M. Gagliolo, J. Schmidhuber, Dynamic algorithm portfolios. Ann. Math. Artif. Intell. 47, 3–4 (2006)

    Article  MathSciNet  Google Scholar 

  37. M. Gebser, B. Kaufmann, T. Schaub, Solution enumeration for projected boolean search problems, in Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (CPAIOR), vol. 5547, ed. by W.-J. van Hoeve, J.N. Hooker (Springer, Berlin Heidelberg, 2009), pp. 71–86

    Google Scholar 

  38. I.P. Gent, H.H. Hoos, P. Prosser, T. Walsh, Morphing: combining structure and randomness, in Proceedings of the National Conference on Artificial Intelligence (AAAI), vol. 9 (AAAI Press, Orlando, 1999), pp. 849–859

    Google Scholar 

  39. C.P. Gomes, B. Selman, Problem structure in the presence of perturbations, in Proceedings of the National Conference on Artificial Intelligence (AAAI) (AAAI Press, New Providence, 1997), pp. 221–226

    Google Scholar 

  40. C.P. Gomes, B. Selman, Algorithm portfolios. Artif. Intell. 126(1–2), 43–62 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  41. Google, Google ROADEF Challenge (2012). http://challenge.roadef.org/2012/en/index.php

  42. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, I.H. Witten, The WEKA data mining software: an update. SIGKDD Explor. Newsl. 11(1), 10–18 (2009)

    Article  Google Scholar 

  43. Y. Hamadi, S. Jabbour, L. Sais, LySAT: solver description. SAT Competition (2009)

    Google Scholar 

  44. G. Hamerly, C. Elkan, Learning the K in K-means, in Neural Information Processing Systems (MIT Press, Cambridge, 2003)

    Google Scholar 

  45. M. Heule, H. van Marren, march hi: Solver description. SAT Competition (2009)

    Google Scholar 

  46. M. Heule, H. van Marren, march nn (2009). http://www.st.ewi.tudelft.nl/sat/download.php

  47. M. Heule, M. Dufour, J. Van Zwieten, H. Van Maaren, March eq: implementing additional reasoning into an efficient lookahead SAT solver, in Proceedings of the International Conference on Theory and Application of Satisfiability Testing (SAT), vol. 3542 (Springer, Berlin, 2005), pp. 345–359

    Google Scholar 

  48. K.L. Hoffman, M. Padberg, Solving airline crew scheduling problems by branch-and-cut. Manag. Sci. 39(6), 657–682 (1993)

    Article  MATH  Google Scholar 

  49. H.H. Hoos, An adaptive noise mechanism for WalkSAT, in Proceedings of the National Conference on Artificial Intelligence (AAAI) (AAAI Press, Menlo Park, 2002), pp. 655–660

    Google Scholar 

  50. E. Housos, T. Elmroth, Automatic optimization of subproblems in scheduling airline crews. Interfaces 27(5), 68–77 (1997)

    Article  Google Scholar 

  51. B.A. Huberman, R.M. Lukose, T. Hogg, An economic approach to hard computational problems. Science 275(5296), 51–53 (1997)

    Article  Google Scholar 

  52. L. Hubert, P. Arabie, Comparing partitions. J. Classif. 2(1), 193–218 (1985)

    Article  Google Scholar 

  53. F. Hutter, Y. Hamadi, Parameter adjustment based on performance prediction: towards an instance-aware problem solver. Technical Report MSR-TR-2005-125, Microsoft Research (2005)

    Google Scholar 

  54. F. Hutter, Y. Hamadi, H.H. Hoos, K. Leyton-Brown, Performance prediction and automated tuning of randomized and parametric algorithms, in Proceedings of Principles and Practice of Constraint Programming (CP), vol. 4204, ed. by F. Benhamou (Springer, Berlin, Heidelberg, 2006), pp. 213–228

    Google Scholar 

  55. F. Hutter, H.H. Hoos, K. Leyton-Brown, K. Murphy, Time-bounded sequential parameter pptimization, in Learning and Intelligent Optimization (LION), vol. 6073, ed. by C. Blum, R. Battiti (Springer, Berlin, Heidelberg, 2010), pp. 281–298

    Google Scholar 

  56. F. Hutter, H.H. Hoos, K. Leyton-Brown, T. Stützle, ParamILS: an automatic algorithm configuration framework. J. Artif. Intell. Res. 36(1), 267–306 (2009)

    MATH  Google Scholar 

  57. F. Hutter, D.A.D. Tompkins, H.H. Hoos, Scaling and probabilistic smoothing: efficient dynamic local search for SAT, in Principles and Practice of Constraint Programming (CP), vol. 2470, ed. by P.V. Hentenryck (Springer, Berlin Heidelberg, 2002), pp. 233–248

    Google Scholar 

  58. F. Hutter, D.A.D. Tompkins, H.H. Hoos, Scaling and probabilistic smoothing: efficient dynamic local search for SAT, in Proceedings of the International Conference on Principles and Practice of Constraint Programming (CP) (Springer, Berlin, 2002), pp. 233–248

    Google Scholar 

  59. IBM, Reference manual and user manual. V12.5 (2013)

    Google Scholar 

  60. S. Kadioglu, Y. Malitsky, M. Sellmann, K. Tierney, ISAC – instance-specific algorithm configuration, in Proceedings of the European Conference on Artificial Intelligence (ECAI) (IOS Press, Amsterdam, 2010), pp. 751–756

    Google Scholar 

  61. S. Kadioglu, M. Sellmann, Dialectic search, in Proceedings of Principles and Practice of Constraint Programming (CP), vol. 5732, ed. by I. Gent (Springer, Berlin Heidelberg, 2009), pp. 486–500

    Google Scholar 

  62. A.R. Khudabukhsh, L. Xu, H.H. Hoos, K. Leyton-Brown, SATenstein: automatically building local search SAT solvers from components, in International Joint Conference on Artificial Intelligence (IJCAI) (AAAI Press, Menlo Park, 2009), pp. 517–524

    Google Scholar 

  63. T. Koch, T. Achterberg, E. Andersen, O. Bastert, T. Berthold, R.E. Bixby, E. Danna, G. Gamrath, A.M. Gleixner, S. Heinz, A. Lodi, H. Mittelmann, T. Ralphs, D. Salvagnin, D.E. Steffy, K. Wolter, MIPLIB 2010 - Mixed Integer Programming Library version 5. Math. Program. Comput. 3(2), 103–163 (2011)

    Article  MathSciNet  Google Scholar 

  64. M.G. Lagoudakis, M.L. Littman, Learning to select branching rules in the DPLL procedure for satisfiability, in LICS 2001 Workshop on Theory and Applications of Satisfiability Testing (SAT), vol. 9 (2001), pp. 344–359

    Google Scholar 

  65. D.H. Leventhal, M. Sellmann, The accuracy of search heuristics: an empirical study on knapsack problems, in Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (CPAIOR), vol. 5015, ed. by L. Perron, M.A. Trick (Springer, Berlin, Heidelberg, 2008), pp. 142–157

    Google Scholar 

  66. K. Leyton-Brown, E. Nudelman, G. Andrew, J. Mcfadden, Y. Shoham, A portfolio approach to algorithm selection, in Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) (Morgan Kaufmann Publishers Inc., San Francisco, 2003), pp. 1542–1543

    Google Scholar 

  67. K. Leyton-Brown, E. Nudelman, G. Andrew, J. Mcfadden, Y. Shoham, Boosting as a Metaphor for Algorithm Design, in Proceedings of Principles and Practice of Constraint Programming (CP), vol. 2833, ed. by F. Rossi (Springer, Berlin, Heidelberg, 2003), pp. 899–903

    Google Scholar 

  68. K. Leyton-Brown, M. Pearson, Y. Shoham, Towards a universal test suite for combinatorial auction algorithms, in Proceedings of the ACM Conference on Electronic Commerce (ACM Press, New York, 2000), pp. 66–76

    Google Scholar 

  69. C.M. Li, W.Q. Huang, G2WSAT: gradient-based greedy WalkSAT, in Proceedings of the International Conference on Theory and Application of Satisfiability Testing (SAT), vol. 3569 (Springer, Berlin, 2005), pp. 158–172

    Google Scholar 

  70. C.M. Li and W. We. Combining Adaptive Noise and Promising Decreasing Variables in Local Search for SAT. Solver description. SAT Competition (2009)

    Google Scholar 

  71. X. Li, M.J. Garzarán, D. Padua, Optimizing sorting with genetic algorithms. International Symposium on Code Generation and Optimization (CGO) (2005), pp. 99–110

    Google Scholar 

  72. S.P. Lloyd, Least squares quantization in PCM. Trans. Inf. Theory 28(2), 129–137 (1982)

    Article  MATH  MathSciNet  Google Scholar 

  73. L. Lobjois, M. Lemaître, Branch and bound algorithm selection by performance prediction, in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) (AAAI Press., Madison, 1998), pp. 353–358

    Google Scholar 

  74. Y. Malitsky, D. Mehta, B. O’Sullivan, H. Simonis, Tuning parameters of large neighborhood search for the machine reassignment problem, in Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (CPAIOR), vol. 7874, ed. by C. Gomes, M. Sellmann (Springer, Berlin, Heidelberg, 2013), pp. 176–192

    Google Scholar 

  75. Y. Malitsky, A. Sabharwal, H. Samulowitz, M. Sellmann, Algorithm portfolios based on cost-sensitive hierarchical clustering, in Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) (AAAI Press, Beijing, 2013), pp. 608–614

    Google Scholar 

  76. Y. Malitsky, M. Sellmann, Stochastic offline programming, in International Conference on Tools with Artificial Intelligence (ICTAI) (IEEE Press, New Yark, 2009), pp. 784–791

    Google Scholar 

  77. Y. Malitsky, M. Sellmann, Instance-specific algorithm configuration as a method for non-model-based portfolio generation, in Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (CPAIOR), vol. 7298, ed. by N. Beldiceanu, N. Jussien, E. Pinson (Springer, Berlin, Heidelberg, 2012), pp. 244–259

    Google Scholar 

  78. D. McAllester, B. Selman, H. Kautz, Evidence for invariants in local search, in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) (AAAI Press, Providence, 1997), pp. 321–326

    Google Scholar 

  79. D. Mehta, B. O’Sullivan, H. Simonis, Comparing solution methods for the machine reassignment problem, in Principles and Practice of Constraint Programming (CP), vol. 7514, ed. by M. Milano (Springer, Berlin, Heidelberg, 2012), pp. 782–797

    Google Scholar 

  80. S. Minton, Automatically configuring constraint satisfaction programs: a case study. Constraints 1(1–2), 7–43 (1996)

    Article  MathSciNet  Google Scholar 

  81. M. Motoki, R. Uehara, Unique solution instance generation for the 3-Satisfiability (3SAT) problem. Technical Report COMP98-54, IEICE (1998)

    Google Scholar 

  82. M. Muja, D.G. Lowe, Fast approximate nearest neighbors with automatic algorithm configuration, in VISAPP International Conference on Computer Vision Theory and Applications (2009), pp. 331–340

    Google Scholar 

  83. N. Musliu, Local search algorithm for unicost set covering problem, in Proceedings of the International Conference on Advances in Applied Artificial Intelligence: Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE) (Springer, Berlin, 2006), pp. 302–311

    Google Scholar 

  84. M. Nikolic, F. Maric, P. Janici, Instance based selection of policies for SAT solvers, in Proceedings of the International Conference on Theory and Applications of Satisfiability Testing (SAT) (Springer, Berlin, 2009), pp. 326–340

    Google Scholar 

  85. E. Nudelman, A. Devkar, Y. Shoham, K. Leyton-Brown, Understanding random SAT: beyond the clauses-to-variables ratio, in Principles and Practice of Constraint Programming (CP), vol. 3258, ed. by M. Wallace (Springer, Berlin, Heidelberg, 2004), pp. 438–452

    Google Scholar 

  86. M. Oltean, Evolving evolutionary algorithms using linear genetic programming. Evol. Comput. 13(3), 387–410 (2005)

    Article  Google Scholar 

  87. E. O’Mahony, E. Hebrard, A. Holland, C. Nugent, B. O’ullivan, Using case-based reasoning in an algorithm portfolio for constraint solvingm, in Irish Conference on Artificial Intelligence and Cognitive Science (2008)

    Google Scholar 

  88. D.J. Patterson, H. Kautz, Auto-walksat: a self-tuning implementation of walksat, in LICS Workshop on Theory and Applications of Satisfiability Testing (SAT), vol. 9 (2001), pp. 360–368

    Google Scholar 

  89. D. Pelleg, A.W. Moore, X-means: extending K-means with efficient estimation of the number of clusters, in Proceedings of the Seventeenth International Conference on Machine Learning (ICML) (Morgan Kaufmann Publishers Inc., San Francisco, 2000), pp. 727–734

    Google Scholar 

  90. M.P. Petrik, S. Zilberstein, Learning static parallel portfolios of algorithms, in International Symposium on Artificial Intelligence and Mathematics (ISAIM) (2006)

    Google Scholar 

  91. D.N. Pham, A. Anbulagan, Resolution enhanced SLS solver: R+AdaptNovelty+. solver description. SAT Competition (2007)

    Google Scholar 

  92. D.N. Pham, C. Gretton, gnovelty+. Solver description. SAT Competition (2007)

    Google Scholar 

  93. D.N. Pham, C. Gretton, gnovelty+ (v.2). Solver description. SAT Competition (2009)

    Google Scholar 

  94. S. Prestwich, Random walk with continuously smoothed variable weights, in Theory and Applications of Satisfiability Testing (SAT), vol. 3569, ed. by F. Bacchus, T. Walsh (Springer, Berlin, Heidelberg, 2005), pp. 203–215

    Google Scholar 

  95. W.M. Rand, Objective criteria for the evaluation of clustering methods. J. Am. Stat. Assoc. 66(336), 846–850 (1971)

    Article  Google Scholar 

  96. P. Refalo, Impact-based search strategies for constraint programming, in Principles and Practice of Constraint Programming (CP), vol. 3258, M. Wallace (Springer, Berlin, Heidelberg, 2004), pp. 557–571

    Google Scholar 

  97. L. Rokach, O. Maimon, Clustering methods. The Data Mining and Knowledge Discovery Handbook (Springer, New York, 2005), pp. 321–352

    Google Scholar 

  98. O. Roussel, Description of ppfolio (2011). http://www.cril.univ-artois.fr/~roussel/ppfolio/solver1.pdf

  99. H. Samulowitz, R. Memisevic, Learning to solve QBF, in Proceedings of the National Conference on Artificial Intelligence (AAAI) (AAAI Press, Menlo Park, 2007), pp. 255–260

    Google Scholar 

  100. A. Saxena, MIP Benchmark Instances (2003). http://www.andrew.cmu.edu/user/anureets/mpsInstances.htm

  101. M. Sellmann, Disco-novo-gogo: integrating local search and complete search with restarts, in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) (AAAI Press, Boston, 2006), pp. 1051–1056

    Google Scholar 

  102. B. Selman, H. Kautz, Domain-independent extensions to GSAT: solving large structured satisfiability problems, in Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) (Morgan Kaufmann Publishers Inc., San Francisco, 1993.), pp. 290–295

    Google Scholar 

  103. B. Silverthorn, R. Miikkulainen, Latent class models for algorithm portfolio methods, in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) (AAAI Press, Atlanta, 2010)

    Google Scholar 

  104. A. Slater, Modeling more realistic SAT problems, in Advances in Artificial Intelligence, vol. 2557, ed. by B. McKay, J. Slaney (Springer, Berlin, Heidelberg, 2002), pp. 291–602

    Google Scholar 

  105. K. Smith-Miles, Cross-disciplinary perspectives on meta-learning for algorithm selection. ACM Comput. Surv. 41(1), 6:1–6:25 (2009)

    Google Scholar 

  106. M. Soos, CryptoMiniSat 2.5.0. Solver description. SAT Race (2010)

    Google Scholar 

  107. M. Soos, Cryptominisat 2.9.0 (2011)

    Google Scholar 

  108. N. Sorensson, N. Een, MiniSAT 2.2.0 (2010). http://minisat.se

  109. D. Stern, H. Samulowitz, R. Herbrich, T. Graepel, L. Pulina, A. Tacchella, Collaborative expert portfolio management, in Proceedings of the National Conference on Artificial Intelligence (AAAI) (AAAI Press, Atlanta, 2010)

    Google Scholar 

  110. M. Streeter, D. Golovin, S.F. Smith, Combining multiple heuristics online, in Proceedings of the National Conference on Artificial Intelligence (AAAI) (AAAI Press, Vancouver, 2007), pp. 1197–1203

    Google Scholar 

  111. M.J. Streeter, S.F. Smith, New techniques for algorithm portfolio design, in Proceedings of the Conference in Uncertainty in Artificial Intelligence (UAI), ed. by D.A. McAllester, P. Myllymäki (AUAI Press, Helsinki, 2008), pp. 519–527

    Google Scholar 

  112. H. Terashima-Marín, P. Ross, Evolution of constraint satisfaction strategies in examination timetabling, in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), vol. 1, ed. by W. Banzhaf, J. Daida, A.E. Eiben, M.H. Garzon, V. Honavar, M. Jakiela, R.E. Smith (Morgan Kaufmann, San Francisco, 1999), pp. 635–642

    Google Scholar 

  113. J. Thornton, D.N. Pham, S. Bain, V. Ferreira, Additive versus multiplicative clause weighting for SAT, in Proceedings of the National Conference on Artificial Intelligence (AAAI) (AAAI Press, San Jose, 2004), pp. 191–196

    Google Scholar 

  114. D.A.D Tompkins, F. Hutter, H.H. Hoos, saps. Solver description. SAT Competition (2007)

    Google Scholar 

  115. C. Toregas, R. Swain, C. ReVelle, L. Bergman, The location of emergency service facilities. Oper. Res. 19(6), 1363–1373 (1971)

    Article  MATH  Google Scholar 

  116. T. Uchida, O. Watanabe, Hard SAT instance generation based on the factorization problem (2010). http://www.is.titech.ac.jp/~watanabe/gensat/a2/index.html

  117. F.J. Vasko, F.E. Wolf, K.L. Stott, Optimal selection of ingot sizes via set covering. Oper. Res. 35(3), 346–353 (1987)

    Article  Google Scholar 

  118. W. Wei, C.M. Li, Switching between two adaptive noise mechanisms in local search for SAT. Solver description. SAT Competition (2009)

    Google Scholar 

  119. W. Wei, C.M. Li, H. Zhang, adaptg2wsatp. Solver description. SAT Competition (2007)

    Google Scholar 

  120. W. Wei, C.M. Li, H. Zhang, Combining adaptive noise and promising decreasing variables in local search for SAT. Solver description. SAT Competition (2007)

    Google Scholar 

  121. W. Wei, C.M. Li, H. Zhang, Deterministic and random selection of variables in local search for SAT. Solver description. SAT Competition (2007)

    Google Scholar 

  122. D.H. Wolpert, W.G. Macready, No free lunch theorems for optimization. Evol. Comput. 1(1), 67–82 (1997)

    Article  Google Scholar 

  123. L. Xu, F. Hutter, H.H. Hoos, K. Leyton-Brown, SATzilla2009: an automatic algorithm portfolio for SAT. Solver description. SAT Competition (2009)

    Google Scholar 

  124. L. Xu, H.H. Hoos, K. Leyton-Brown. Hydra: automatically configuring algorithms for portfolio-based selection, in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) (AAAI Press, Atlanta, 2010)

    Google Scholar 

  125. L. Xu, F. Hutter, H.H. Hoos, K. Leyton-Brown, SATzilla-07: the design and analysis of an algorithm portfolio for SAT, in Proceedings of Principles and Practice of Constraint Programming (CP), vol. 4741, ed. by C. Bessiere (Springer, Berlin, Heidelberg, 2007), pp. 712–727.

    Google Scholar 

  126. L. Xu, F. Hutter, H.H. Hoos, K. Leyton-Brown, SATzilla: portfolio-based algorithm selection for SAT. J. Artif. Intell. Res. 32(1), 565–606 (2008)

    MATH  Google Scholar 

  127. L. Xu, F. Hutter, J. Shen, H.H. Hoos, K. Leyton-Brown, SATzilla2012: improved algorithm selection based on cost-sensitive classification models. SAT Competition (2012)

    Google Scholar 

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    Yuri Malitsky

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Malitsky, Y. (2014). ISAC for Algorithm Selection. In: Instance-Specific Algorithm Configuration. Springer, Cham. https://doi.org/10.1007/978-3-319-11230-5_5

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  • DOI: https://doi.org/10.1007/978-3-319-11230-5_5

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