# 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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