Automatically Produced Algorithms for the Generalized Minimum Spanning Tree Problem

Carlos Contreras-Bolton,1 Carlos Rey,2 Sergio Ramos-Cossio,2 Claudio Rodríguez,2 Felipe Gatica,2 and Victor Parada2

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

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 genotypephenotype
genetic algorithmto 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.


http://www.hindawi.com/journals/sp/2016/1682925/