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

  • Pérez-Ortega, Joaquín
  • R., Rodolfo A. Pazos
  • Ruiz-Vanoye, Jorge A.
  • Frausto-Solís, Juan
  • González-Barbosa, Juan J.
  • Fraire-Huacuja, Hector J.
  • Díaz-Parra, Ocotlán
Abstract:
The only metric that had existed so far to determine the best algorithm for solving an general Asymmetric Traveling Salesman Problem (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. In this paper we propose: (1) the use of a genetic distance metric for improving the selection of the algorithms that best solve a given instance of the ATSP and (2) the use of discriminant analysis as a means for predictive learning (data-mining techniques) aiming at selecting meta-heuristic algorithms.
Research areas:
Year:
2010
Type of Publication:
Article
Keywords:
Inductive learning; discriminant analysis; data-mining techniques; machine learning; genetic distance metric
Journal:
Journal of Intelligent & Fuzzy Systems
Volume:
21
Number:
1-2
Pages:
57-64
ISSN:
1064-1246
DOI:
10.3233/IFS-2010-0435
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