Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
Merz, P., Freisleben, B.: Fitness Landscapes and Memetic Algorithm Design. New Ideas in Optimization, pp. 245–260. McGraw-Hill Ltd. (1999)
Wolpert, D.H., Macready, W.G.: No Free Lunch Theorems for Optimization. IEEE Transactions on Evolutionary Computation 1, 67–82 (1997)
Cohen, P.: Empirical Methods for Artificial Intelligence. The MIT Press, Cambridge (1995)
Hoos, H.: Stochastic Local Search Methods, Models, Applications, PhD Thesis, Department of Computer Science from Darmstadt University of Technology (1998)
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)
Lemeire, J., Dirkx, E.: Causal Models for Parallel Performance Analysis. In: 4th PA3CT Symposium, Edegem, Belgium (2004)
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)
Hoos, H.: Stochastic Local Search Methods, Models, Applications, PhD Thesis, Department of Computer Science from Darmstadt University of Technology (1998)
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)
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)
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)
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)
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)
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)
Beasley, J.E.: OR-Library. Brunel University (2006), http://people.brunel.ac.uk/~mastjjb/jeb/orlib/binpackinfo.html
Scholl, A., Klein, R.: (2003), http://www.wiwi.uni-jena.de/Entscheidung/binpp/
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)
Spirtes, P., Glymour, C., Scheines, R.: Causation, Prediction, and Search, 2nd edn. MIT Press, Cambridge (2001)
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)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pérez, J. et al. (2008). An Application of Causality for Representing and Providing Formal Explanations about the Behavior of the Threshold Accepting Algorithm. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2008. ICAISC 2008. Lecture Notes in Computer Science(), vol 5097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69731-2_102
Download citation
DOI: https://doi.org/10.1007/978-3-540-69731-2_102
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69572-1
Online ISBN: 978-3-540-69731-2
eBook Packages: Computer ScienceComputer Science (R0)