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

DOI: 10.1007/978-3-540-85563-7_19

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5177)
Cite this paper as:
Ruiz-Vanoye J.A., Díaz-Parra O., N. V.L. (2008) A Metric to Discriminate the Selection of Algorithms for the General ATSP Problem. In: Lovrek I., Howlett R.J., Jain L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2008. Lecture Notes in Computer Science, vol 5177. Springer, Berlin, Heidelberg

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 

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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

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