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Scientific Programming
Volume 2016 (2016), Article ID 1682925, 11 pages
http://dx.doi.org/10.1155/2016/1682925
Research Article

Automatically Produced Algorithms for the Generalized Minimum Spanning Tree Problem

1DEI, University of Bologna, Viale Risorgimento 2, 40136 Bologna, Italy
2Departamento de Ingeniería Informática, Universidad de Santiago de Chile, 3659 Avenida Ecuador, 9170124 Santiago, Chile

Received 2 December 2015; Revised 2 February 2016; Accepted 16 February 2016

Academic Editor: Frédéric Saubion

Copyright © 2016 Carlos Contreras-Bolton et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The generalized minimum spanning tree problem consists of finding a minimum cost spanning tree in an undirected graph for which the vertices are divided into clusters. Such spanning tree includes only one vertex from each cluster. Despite the diverse practical applications for this problem, the NP-hardness continues to be a computational challenge. Good quality solutions for some instances of the problem have been found by combining specific heuristics or by including them within a metaheuristic. However studied combinations correspond to a subset of all possible combinations. In this study a technique based on a genotype-phenotype genetic algorithm to automatically construct new algorithms for the problem, which contain combinations of heuristics, is presented. The produced algorithms are competitive in terms of the quality of the solution obtained. This emerges from the comparison of the performance with problem-specific heuristics and with metaheuristic approaches.