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

  • Ruiz-Vanoye, Jorge A.
  • Díaz-Parra, Ocotlán
  • Najera, Vanesa Landero
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.
Research areas:
Year:
2008
Type of Publication:
In Book
Keywords:
Inductive learning; genetic algorithm; discriminant analysis; data-mining techniques; machine learning
Editor:
Ignac Lovrek, Robert J. Howlett, Lakhmi C. Jain
Volume:
5177
Chapter:
19
Pages:
106-113
Publisher:
Springer Berlin Heidelberg
Series:
Lecture Notes in Computer Science
ISBN:
978-3-540-85562-0
DOI:
10.1007/978-3-540-85563-7_19
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