Using meta-learning to recommend meta-heuristics for the traveling salesman problem

• Type: Article in International Conference Proceedings Book
• Date: 2011

Authors

Kanda, JY (Author) Other; De Carvalho, ACPLF (Author) Other; Hruschka, ER (Author) Other; Soares, C (Author) FEP

Conference proceedings International

Title: Proceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011

Pages: 346-351

10th International Conference on Machine Learning and Applications, ICMLA 2011

Honolulu, HI, 18 December 2011 through 21 December 2011

Indexing

ISI Web of Knowledge

Scopus - 14 Citations

Other information

Authenticus ID: P-008-2W6

DOI: 10.1109/icmla.2011.153

Abstract (EN): Several optimization methods can find good solutions for different instances of the Traveling Salesman Problem (TSP). Since there is no method that generates the best solution for all instances, the selection of the most promising method for a given TSP instance is a difficult task. This paper describes a meta-learning-based approach to select optimization methods for the TSP. Multilayer perceptron (MLP) networks are trained with TSP examples. These examples are described by a set of TSP characteristics and the cost of solutions obtained by a set of optimization methods. The trained MLP network model is then used to predict a ranking of these methods for a new TSP instance. Correlation measures are used to compare the predicted ranking with the ranking previously known. The obtained results suggest that the proposed approach is promising. © 2011 IEEE.

Language: English

Type (Professor's evaluation): Scientific