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 that the supremacy of algorithms only applied to a subset of instances. However, it was not explained why an algorithm solved better an instances subset. In this respect, this work approaches the problem of explaining through causal modeling 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 Tabu Search algorithm applied to 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-hard problems.
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)
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)
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)
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)
Hoos, H., Smyth, K., Stutzle, T.: Search Space Features Underlying the Performance of Stochastic Local Search Algorithms for MAX-SAT. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 51–60. Springer, Heidelberg (2004)
Lemeire, J., Dirkx, E.: Causal Models for Parallel Performance Analysis. In: 4th PA3CT Symposium, Edegem, Belgium (2004)
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)
Glover, F.: Tabu Search - Part I, First Comprehensive Description of Tabu Search. ORSA-Journal on Computing 1(3), 190–206 (1989)
Beasley, J.E.: OR-Library. Brunel University (2006), http://people.brunel.ac.uk/~mastjjb/jeb/orlib/binpackinfo.html
Scholl, A., Klein, R.: http://www.wiwi.uni-jena.de/Entscheidung/binpp/ (2003)
Falkenauer, E., Delchambre, A.: A Genetic Algorithm for Bin Packing and Line Balancing. In: Proceedings of the IEEE 1992 International Conference on Robotics and Automation, pp. 1186–1192. IEEE Computer Society Press, Los Alamitos (1992)
Fleszar, K., Hindi, K.S.: New Heuristics for One-dimensional Bin Packing. Computers and Operations Research 29, 821–839 (2002)
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. The MIT Press, Cambridge (2001)
Lauritzen, S.L.: The EM algorithm for Graphical Association Models with Missing Data. Computational Statistics Data Analysis 19, 191–201 (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). A Causal Approach for Explaining Why a Heuristic Algorithm Outperforms Another in Solving an Instance Set of the Bin Packing Problem. In: An, A., Matwin, S., Raś, Z.W., Ślęzak, D. (eds) Foundations of Intelligent Systems. ISMIS 2008. Lecture Notes in Computer Science(), vol 4994. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68123-6_64
Download citation
DOI: https://doi.org/10.1007/978-3-540-68123-6_64
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68122-9
Online ISBN: 978-3-540-68123-6
eBook Packages: Computer ScienceComputer Science (R0)