A Metric to Discriminate the Selection of Algorithms for the General ATSP Problem

  • Jorge A. Ruiz-Vanoye
  • Ocotlán Díaz-Parra
  • Vanesa Landero N.
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5177)

Abstract

In this paper we propose: (1) the use of discriminant analysis as a means for predictive learning (data-mining techniques) aiming at selecting metaheuristic algorithms and (2) the use of a metric for improving the selection of the algorithms that best solve a given instance of the Asymmetric Traveling Salesman Problem (ATSP). The only metric that had existed so far to determine the best algorithm for solving an ATSP instance is based on the number of cities; nevertheless, it is not sufficiently adequate for discriminating the best algorithm for solving an ATSP instance, thus the necessity for devising a new metric through the use of data-mining techniques.

Keywords

Inductive learning genetic algorithm discriminant analysis data-mining techniques machine learning 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kirkpatrick, S., Gelatt, C., Vecci, M.: Optimization by Simulated Annealing. Science 220(4598) (1983)Google Scholar
  2. 2.
    Rutenbar, R.: Simulated Annealing Algorithms: An Overview. IEEE Circuits and Devices Magazine 5(5), 19–26 (1989)CrossRefGoogle Scholar
  3. 3.
    Kantardzic, M.: Data Mining: Concepts, Models, Methods, and Algorithms. John Wiley & Sons, Chichester (2003)MATHGoogle Scholar
  4. 4.
    Cirasella, D.S., Cirasella, J., Johnson, D.S., McGeoch, L.A., Zhang, W.: The asymmetric traveling salesman problem: Algorithms, instance generators, and tests. In: Buchsbaum, A.L., Snoeyink, J. (eds.) ALENEX 2001. LNCS, vol. 2153, pp. 32–59. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  5. 5.
    Fink, E.: How to Solve it Automatically, Selection among Problem-solving Methods. In: Proceedings of the Fourth International Conference on AI Planning Systems AIPS 1998, pp. 128–136 (1998)Google Scholar
  6. 6.
    Soares, C., Brazdil, P.: Zoomed Ranking, Selection of Classification Algorithms Based on Relevant Performance Information. In: Zighed, D.A., Komorowski, J., Żytkow, J. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 126–135. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  7. 7.
    Pérez, J., Pazos, R.A., Frausto, J., Rodriguez, G., Romero, D., Cruz, L.: A Statistical Approach for Algorithm Selection. In: Proceedings of III International Workshop on Experimental an Efficient Algorithms, Brazil. LNCS. Springer, Heidelberg (2004)Google Scholar
  8. 8.
    Pérez, J., Pazos, R.A., Frausto, J., Rodriguez, G., Cruz, L.: Self-Tuning Mechanism for Genetic Algorithms Parameters an Application to Data-Object Allocation in the Web. In: Laganá, A., Gavrilova, M.L., Kumar, V., Mun, Y., Tan, C.J.K., Gervasi, O. (eds.) ICCSA 2004. LNCS, vol. 3046. Springer, Heidelberg (2004)Google Scholar
  9. 9.
    Pérez, J., Pazos, R.A., Fraire, H., Cruz, L., Pecero, J.: Adaptive Allocation of Data-Objects in the Web using Neural Networks. LNCS, vol. 2829. Springer, Heidelberg (2003)Google Scholar
  10. 10.
    Pérez, J., Pazos, R.A., Frausto, J., Rodriguez, G., Cruz, L.: Comparison and Selection of Exact and Heuristic Algorithm. In: Laganá, A., Gavrilova, M.L., Kumar, V., Mun, Y., Tan, C.J.K., Gervasi, O. (eds.) ICCSA 2004. LNCS, vol. 3045. Springer, Heidelberg (2004)Google Scholar
  11. 11.
    Pérez, J., Pazos, R.A., Fraire, H., Cruz, L., Santiago, E., García, N.E.: A Machine Learning Ap-proach for Modeling Algorithm Performance Predictors. In: Torra, V., Narukawa, Y. (eds.) MDAI 2004. LNCS (LNAI), vol. 3131. Springer, Heidelberg (2004)Google Scholar
  12. 12.
    Wagner, S., Affenzeller, M.: The HeuristicLab Optimization Environment. Technical Report. Institute of Formal Models and Verification. Johannes Kepler University Linz. Austria (2004)Google Scholar
  13. 13.
    Affenzeller, M.: New Generic Hybrids Based Upon Genetic Algorithms. In: Garijo, F.J., Riquelme, J.-C., Toro, M. (eds.) IBERAMIA 2002. LNCS (LNAI), vol. 2527, pp. 329–339. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  14. 14.
    Wagner, S., Affenzeller, M.: HeuristicLab: A Generic and Extensible Optimization Environment. In: Adaptive and Natural Computing Algorithms. Springer Computer Science, pp. 538–541. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  15. 15.
    Wagner, S., Affenzeller, M.: HeuristicLab Grid - A Flexible and Extensible Environment for Parallel Heuristic Optimization. In: Proceedings of the 15th International Conference on Systems Science. Oficyna Wydawnicza Politechniki Wroclawskiej, vol. 1, pp. 289–296 (2004)Google Scholar
  16. 16.
    Affenzeller, M.: New Variants of Genetic Algorithms Applied to Problems of Combinatorial Optimization. In: Proceedings of the EMCSR 2002, vol. 1, pp. 75–80 (2002)Google Scholar
  17. 17.
    Reinelt, G.: TSPLIB - A Traveling Salesman Problem Library. ORSA Journal on Computing 3, 376–384 (1991)MATHGoogle Scholar
  18. 18.
    SPSS, Inc. Headquarters, Chicago, Illinois (2008), http://www.spss.com/es/
  19. 19.
    Witten, I.H., Frank, E.: Data Mining Practical Machine Learning Tools an Techniques. Morgan Kaufmann Publishers, Elsevier (2005)Google Scholar
  20. 20.
    Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Jorge A. Ruiz-Vanoye
    • 1
  • Ocotlán Díaz-Parra
    • 2
  • Vanesa Landero N.
    • 1
  1. 1.Centro Nacional de Investigación y Desarrollo Tecnológico, Interior internado Palmira s/n., Col. PalmiraCuernavacaMexico
  2. 2.CIICApUniversidad Autonóma del Estado de MorelosCuernavacaMexico

Personalised recommendations