An Application of Causality for Representing and Providing Formal Explanations about the Behavior of the Threshold Accepting Algorithm

  • Joaquín Pérez
  • Laura Cruz
  • Rodolfo Pazos
  • Vanesa Landero
  • Gerardo Reyes
  • Héctor Fraire
  • Juan Frausto
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5097)


The problem of algorithm selection for solving NP problems arises with the appearance of a variety of heuristic algorithms. The first works claimed the supremacy of some algorithm for a given problem. Subsequent works revealed the supremacy of algorithms only applied to a subset of instances. However, it was not explained why an algorithm solved better a subset of instances. In this respect, this work approaches the problem of explaining through causal model the interrelations between instances characteristics and the inner workings of algorithms. For validating the results of the proposed approach, a set of experiments was carried out in a study case of the Threshold Accepting algorithm to solve the Bin Packing problem. Finally, the proposed approach can be useful for redesigning the logic of heuristic algorithms and for justifying the use of an algorithm to solve an instance subset. This information could contribute to algorithm selection for NP problems.


Heuristic Algorithm Problem Instance Causal Model Causal Analysis Instance Subset 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Garey, M.R., Jhonson, D.S.: Computers and Intractability, a Guide to the Theory of NP-completeness. W. H. Freeman and Company, New York (1979)zbMATHGoogle Scholar
  2. 2.
    Merz, P., Freisleben, B.: Fitness Landscapes and Memetic Algorithm Design. New Ideas in Optimization, pp. 245–260. McGraw-Hill Ltd. (1999)Google Scholar
  3. 3.
    Wolpert, D.H., Macready, W.G.: No Free Lunch Theorems for Optimization. IEEE Transactions on Evolutionary Computation 1, 67–82 (1997)CrossRefGoogle Scholar
  4. 4.
    Cohen, P.: Empirical Methods for Artificial Intelligence. The MIT Press, Cambridge (1995)zbMATHGoogle Scholar
  5. 5.
    Hoos, H.: Stochastic Local Search Methods, Models, Applications, PhD Thesis, Department of Computer Science from Darmstadt University of Technology (1998)Google Scholar
  6. 6.
    Pérez, O., Pazos, R.: A Statistical Approach for Algorithm Selection. In: Ribeiro, C.C., Martins, S.L. (eds.) WEA 2004. LNCS, vol. 3059, pp. 417–431. Springer, Heidelberg (2004)Google Scholar
  7. 7.
    Lemeire, J., Dirkx, E.: Causal Models for Parallel Performance Analysis. In: 4th PA3CT Symposium, Edegem, Belgium (2004)Google Scholar
  8. 8.
    Soares, C., Pinto, J.: Ranking Learning Algorithms: Using IBL and Meta-Learning on Accuracy and Time Results. Journal of Machine Learning 50(3), 251–277 (2003)zbMATHCrossRefGoogle Scholar
  9. 9.
    Hoos, H.: Stochastic Local Search Methods, Models, Applications, PhD Thesis, Department of Computer Science from Darmstadt University of Technology (1998)Google Scholar
  10. 10.
    Konak, A.: Simulation Optimization Using Tabu Search: An Empirical Study. In: Steiger, M., Armstrong, F., Joines, B.,, J.A. (eds.) Proceedings of the 37th Conference on Winter Simulation, pp. 2686–2692 (2005)Google Scholar
  11. 11.
    Pérez, J., Cruz, L., Landero, V., Pazos, R.: Explaining Performance of the Threshold Accepting Algorithm for the Bin Packing Problem: A Causal Approach. In: Proceedings of 14th International Multi-conference, Advanced Computer Systems, Polland (2007)Google Scholar
  12. 12.
    Pérez, J., Pazos, R.: Comparison and Selection of Exact and Heuristic Algorithms. In: Laganá, A., Gavrilova, M.L., Kumar, V., Mun, Y., Tan, C.J.K., Gervasi, O. (eds.) ICCSA 2004. LNCS, vol. 3045, pp. 415–424. Springer, Heidelberg (2004)Google Scholar
  13. 13.
    Pérez, J., Pazos, R.: A Machine Learning Approach for Modeling Algorithm Performance Predictors. In: Torra, V., Narukawa, Y. (eds.) MDAI 2004. LNCS (LNAI), vol. 3131, pp. 70–80. Springer, Heidelberg (2004)Google Scholar
  14. 14.
    Sanvicente, H., Frausto, J.: A Method to Establish the Cooling Scheme in Simulated Annealing Like Algorithms. In: International Conference on Computational Science and Applications. LNCS, vol. 3, pp. 755–763. Springer, Heidelberg (2004)Google Scholar
  15. 15.
    Fleszar, K., Hindi, K.S.: New Heuristics for One-dimensional Bin Packing. In: Computers and Operations Research, vol. 29, pp. 821–839. Elsevier Science Ltd., Amsterdam (2002)Google Scholar
  16. 16.
    Beasley, J.E.: OR-Library. Brunel University (2006),
  17. 17.
    Scholl, A., Klein, R.: (2003),
  18. 18.
    Fayyad, U.M., Irani, K.B.: Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning. In: 13th International Joint Conference of Artificial Intelligence, pp. 1022–1029 (1993)Google Scholar
  19. 19.
    Spirtes, P., Glymour, C., Scheines, R.: Causation, Prediction, and Search, 2nd edn. MIT Press, Cambridge (2001)zbMATHGoogle Scholar
  20. 20.
    Lauritzen, S.: The EM algorithm for Graphical Association Models with Missing Data. In: Computational Statistics Data Analysis, vol. 19, pp. 191–201. Elsevier Science, Amsterdam (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Joaquín Pérez
    • 1
  • Laura Cruz
    • 2
  • Rodolfo Pazos
    • 1
  • Vanesa Landero
    • 1
  • Gerardo Reyes
    • 1
  • Héctor Fraire
    • 1
  • Juan Frausto
    • 3
  1. 1.Departamento de Ciencias Computacionales AP 5-164Centro Nacional de Investigación y Desarrollo Tecnológico (CENIDET)CuernavacaMéxico
  2. 2.División de Estudios de Posgrado e InvestigaciónInstituto Tecnológico de Ciudad Madero (ITCM)Cd. MaderoMéxico
  3. 3.Departamento de Ciencias ComputacionalesInstituto Tecnológico de Estudios Superiores de Monterrey (ITESM)CuernavacaMéxico

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