A genetic distance metric to discriminate the selection of algorithms for the general ATSP problem

Article type: Research Article

Authors: Pérez-Ortega, J. | Pazos, R.A. | Ruiz-Vanoye, J.A. | Frausto-Solís, J. | González-Barbosa, J.J. | Fraire-Huacuja, H.J. | Díaz-Parra, O.

Affiliations: Centro Nacional de Investigación y Desarrollo Tecnológico, Cuernavaca, Mexico | Universidad Juárez Autónoma de Tabasco, Cunduacán, Tabasco, México | Tecnológico de Monterrey Campus Cuernavaca, Cuernavaca, Mexico | Instituto Tecnológico de Cd. Madero, Cd. Madero, Mexico

Note: [] Corresponding author. E-mails: jpo cenidet@yahoo.com.mx (J. Pérez-Ortega); r_pazos_r@yahoo.com.mx (R.A. Pazos R.); jruizvanoye@yahoo.com.mx (J.A. Ruiz-Vanoye); juan.frausto@itesm.mx (J. Frausto-Solís); jjgonzalezbarbosa@gmail.com (J.J. González-Barbosa); hfraire@prodigy.net.mx (H.J. Fraire-Huacuja); koko diazparra@yahoo.com.mx (O. Díaz-Parra).

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.

Keywords: Inductive learning, discriminant analysis, data-mining techniques, machine learning, genetic distance metric

DOI: 10.3233/IFS-2010-0435

Journal: Journal of Intelligent & Fuzzy Systems, vol. 21, no. 1, 2, pp. 57-64, 2010