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Advanced Portfolio Techniques

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Data Mining and Constraint Programming

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10101))

Abstract

There exists a proliferation of different approaches to using portfolios and algorithm selection to make solving combinatorial search and optimisation problems more efficient, as surveyed in the previous chapter. In this chapter, we take a look at a detailed case study that leverages transformations between problem representations to make portfolios more effective, followed by extensions to the state of the art that make algorithm selection more robust in practice.

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Notes

  1. 1.

    http://aslib.net.

  2. 2.

    https://github.com/coseal/aslib_data/tree/master/PROTEUS-2014.

  3. 3.

    We note that multiple containers may have the same group, but in order to make containers easily identifiable, in this example we have assigned a different group to each container.

References

  1. CSP Solver Competition Benchmarks. http://www.cril.univ-artois.fr/~lecoutre/benchmarks.html (2009)

  2. Ansótegui, C., Sellmann, M., Tierney, K.: A gender-based genetic algorithm for the automatic configuration of algorithms. In: Gent, I.P. (ed.) CP 2009. LNCS, vol. 5732, pp. 142–157. Springer, Heidelberg (2009). doi:10.1007/978-3-642-04244-7_14

    Chapter  Google Scholar 

  3. Argelich, J., Li, C., Manyà, F., Planes, J.: Maxsat evaluations (2012). www.maxsat.udl.cat

  4. Audemard, G., Simon, L.: Glucose 2.3 in the SAT 2013 competition. In: Proceedings of SAT Competition 2013, p. 42 (2013)

    Google Scholar 

  5. Biere, A.: Lingeling, plingeling and treengeling entering the SAT competition 2013. In: Proceedings of SAT Competition 2013 (2013)

    Google Scholar 

  6. Bortfeldt, A., Forster, F.: A tree search procedure for the container pre-marshalling problem. Eur. J. Oper. Res. 217(3), 531–540 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  7. Carlo, H., Vis, I., Roodbergen, K.: Storage yard operations in container terminals: literature overview, trends, and research directions. Eur. J. Oper. Res. 235(2), 412–430 (2014)

    Article  MATH  Google Scholar 

  8. Data, S.: (2011). http://www.cs.ubc.ca/labs/beta/Projects/SATzilla/

  9. Een, N., Sörensson, N.: Minisat 2.2 (2013). http://minisat.se

  10. Gebser, M., Kaufmann, B., Neumann, A., Schaub, T.: clasp: a conflict-driven answer set solver. In: Baral, C., Brewka, G., Schlipf, J. (eds.) LPNMR 2007. LNCS (LNAI), vol. 4483, pp. 260–265. Springer, Heidelberg (2007). doi:10.1007/978-3-540-72200-7_23

    Chapter  Google Scholar 

  11. Gecode Team: Gecode: Generic Constraint Development Environment (2006). http://www.gecode.org

  12. Ghahramani, Z., Griffiths, T.L., Sollich, P.: Bayesian nonparametric latent feature models. In: World Meeting on Bayesian Statistics (2006)

    Google Scholar 

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

    Article  Google Scholar 

  14. Hebrard, E.: Mistral, a constraint satisfaction library. In: Proceedings of the Third International CSP Solver Competition (2008)

    Google Scholar 

  15. Hebrard, E., O’Mahony, E., O’Sullivan, B.: Constraint programming and combinatorial optimisation in numberjack. In: Lodi, A., Milano, M., Toth, P. (eds.) CPAIOR 2010. LNCS, vol. 6140, pp. 181–185. Springer, Heidelberg (2010). doi:10.1007/978-3-642-13520-0_22

    Chapter  Google Scholar 

  16. Helmert, M., Röger, G., Karpas, E.: Fast downward stone soup: a baseline for building planner portfolios. In: ICAPS (2011)

    Google Scholar 

  17. Hoos, H.: Adaptive novelty+: novelty+ with adaptive noise. In: AAAI (2002)

    Google Scholar 

  18. Hubert, L., Arabie, P.: Comparing partitions. J. Classif. 2(1), 193–218 (1985). http://dx.doi.org/10.1007/BF01908075

    Article  MATH  Google Scholar 

  19. Hutter, F., Tompkins, D., Hoos, H.: Rsaps: reactive scaling and probabilistic smoothing. In: CP (2002)

    Google Scholar 

  20. Kadioglu, S., Malitsky, Y., Sabharwal, A., Samulowitz, H., Sellmann, M.: Algorithm selection and scheduling. In: Lee, J. (ed.) CP 2011. LNCS, vol. 6876, pp. 454–469. Springer, Heidelberg (2011). doi:10.1007/978-3-642-23786-7_35

    Chapter  Google Scholar 

  21. Kadioglu, S., Malitsky, Y., Sellmann, M., Tierney, K.: ISAC - instance-specific algorithm configuration. In: ECAI, pp. 751–756 (2010)

    Google Scholar 

  22. Kotthoff, L.: LLAMA: leveraging learning to automatically manage algorithms. Technical report, June 2013. arXiv:1306.1031, http://arxiv.org/abs/1306.1031

  23. Le Berre, D., Lynce, I.: CSP2SAT4J: a simple CSP to SAT translator. In: Proceedings of the Second International CSP Solver Competition (2008)

    Google Scholar 

  24. Lecoutre, C., Tabary, S.: Abscon 112, toward more robustness. In: Proceedings of the Third International CSP Solver Competition (2008)

    Google Scholar 

  25. Lee, Y., Hsu, N.: An optimization model for the container pre-marshalling problem. Comput. Oper. Res. 34(11), 3295–3313 (2007)

    Article  MATH  Google Scholar 

  26. Lehnfeld, J., Knust, S.: Loading, unloading and premarshalling of stacks in storage areas: survey and classification. Eur. J. Oper. Res. 239(2), 297–312 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  27. Li, C., Huang, W.: G2wsat: gradient-based greedy walksat. SAT 3569, 158–172 (2005)

    Google Scholar 

  28. Malitsky, Y., Sellmann, M.: Instance-specific algorithm configuration as a method for non-model-based portfolio generation. In: Beldiceanu, N., Jussien, N., Pinson, É. (eds.) CPAIOR 2012. LNCS, vol. 7298, pp. 244–259. Springer, Heidelberg (2012). doi:10.1007/978-3-642-29828-8_16

    Chapter  Google Scholar 

  29. Malitsky, Y., Sabharwal, A., Samulowitz, H., Sellmann, M.: Algorithm portfolios based on cost-sensitive hierarchical clustering. In: IJCAI (2013)

    Google Scholar 

  30. Manthey, N.: The SAT solver RISS3G at SC 2013. In: Proceedings of SAT Competition 2013, p. 72 (2013)

    Google Scholar 

  31. Martin, C., Porter, M.: The extraordinary SVD. Math. Assoc. Am. 119(10), 838–851 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  32. Nudelman, E., Leyton-Brown, K., Hoos, H.H., Devkar, A., Shoham, Y.: Understanding random SAT: beyond the clauses-to-variables ratio. In: Wallace, M. (ed.) CP 2004. LNCS, vol. 3258, pp. 438–452. Springer, Heidelberg (2004). doi:10.1007/978-3-540-30201-8_33

    Chapter  Google Scholar 

  33. Pham, D., Anbulagan: ranov. Solver description. SAT Competition (2007)

    Google Scholar 

  34. Pham, D., Gretton, C.: gnovelty+. Solver description. SAT Competition (2007)

    Google Scholar 

  35. Prasantha, H.: Image compression using SVD. In: Conference on Computational Intelligence and Multimedia Applications, pp. 143–145 (2007)

    Google Scholar 

  36. Prestwich, S.: Vw: Variable weighting scheme. SAT (2005)

    Google Scholar 

  37. Rand, W.: Objective criteria for the evaluation of clustering methods. J. Am. Statist. Assoc. 66(336), 846–850 (1971)

    Article  Google Scholar 

  38. Roussel, O., Lecoutre, C.: XML Representation of Constraint Networks: Format XCSP 2.1. CoRR abs/0902.2362 (2009)

    Google Scholar 

  39. Rutz, O.J., Bucklin, R.E., Sonnier, G.P.: A latent instrumental variables approach to modeling keyword conversion in paid search advertising. J. Mark. Res. 49, 306–319 (2012)

    Article  Google Scholar 

  40. Soos, M.: Cryptominisat 2.9.0 (2011)

    Google Scholar 

  41. Stahlbock, R., Voß, S.: Operations research at container terminals: a literature update. OR Spectr. 30(1), 1–52 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  42. Tamura, N., Tanjo, T., Banbara, M.: System description of a SAT-based CSP solver sugar. In: Proceedings of the Third International CSP Solver Competition, pp. 71–75 (2009)

    Google Scholar 

  43. Tanjo, T., Tamura, N., Banbara, M.: Azucar: a SAT-based CSP solver using compact order encoding. In: Cimatti, A., Sebastiani, R. (eds.) SAT 2012. LNCS, vol. 7317, pp. 456–462. Springer, Heidelberg (2012). doi:10.1007/978-3-642-31612-8_37

    Chapter  Google Scholar 

  44. Choco team: choco: an Open Source Java Constraint Programming Library (2008)

    Google Scholar 

  45. Thornton, J., Pham, D., Bain, S., Ferreira, V.: Additive versus multiplicative clause weighting for SAT. In: PRICAI, pp. 405–416 (2008)

    Google Scholar 

  46. Tierney, K., Pacino, D., Voß, S.: Solving the pre-marshalling problem to optimality with A* and IDA*. Technical report, WP#1401, DS&OR Lab, University of Paderborn (2014)

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  51. Xu, L., Hoos, H., Leyton-Brown, K.: Hydra: automatically configuring algorithms for portfolio-based selection. In: AAAI (2010)

    Google Scholar 

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

    Google Scholar 

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

    MATH  Google Scholar 

  54. Yang, W., Yi, D., Xie, Y., Tian, F.: Statistical identification of syndromes feature and structure of disease of western medicine based on general latent structure mode. Chin. J. Integr. Med. 18, 850–861 (2012)

    Article  Google Scholar 

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Acknowledgements

This work is supported by Science Foundation Ireland (SFI) Grant 10/IN.1/I3032 and FP7 FET-Open Grant 284715. The Insight Centre for Data Analytics is supported by SFI Grant SFI/12/RC/2289.

Author information

Authors and Affiliations

  1. Insight Centre for Data Analytics, University College Cork, Cork, Ireland

    Barry Hurley, Yuri Malitsky, Deepak Mehta & Barry O’Sullivan

  2. University of British Columbia, Vancouver, Canada

    Lars Kotthoff

Authors
  1. Barry Hurley
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  2. Lars Kotthoff
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  3. Yuri Malitsky
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  4. Deepak Mehta
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  5. Barry O’Sullivan
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Corresponding author

Correspondence to Lars Kotthoff .

Editor information

Editors and Affiliations

  1. Université Montpellier 2, Montpellier, France

    Christian Bessiere

  2. KU Leuven, Heverlee, Belgium

    Luc De Raedt

  3. University of British Columbia, Vancouver, British Columbia, Canada

    Lars Kotthoff

  4. Université Catholique de Louvain, Louvain-la-Neuve, Belgium

    Siegfried Nijssen

  5. University College Cork, Cork, Ireland

    Barry O'Sullivan

  6. University of Pisa, Pisa, Italy

    Dino Pedreschi

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Hurley, B., Kotthoff, L., Malitsky, Y., Mehta, D., O’Sullivan, B. (2016). Advanced Portfolio Techniques. In: Bessiere, C., De Raedt, L., Kotthoff, L., Nijssen, S., O'Sullivan, B., Pedreschi, D. (eds) Data Mining and Constraint Programming. Lecture Notes in Computer Science(), vol 10101. Springer, Cham. https://doi.org/10.1007/978-3-319-50137-6_8

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  • DOI: https://doi.org/10.1007/978-3-319-50137-6_8

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