Abstract
The generalized minimum spanning tree problem is a natural extension of the classical minimum spanning tree problem, looking for a tree with minimum cost, spanning exactly one node from each of a given number of predefined, mutually exclusive and exhaustive node sets. In this paper we present a memetic algorithms for solving the generalized minimum spanning tree problem that combines the population concept of genetic algorithms with a fast local improvement method. The proposed algorithm is competitive with other heuristics published to date in both solution quality and computation time. The computational results for several benchmarks problems are reported and the results point out that the memetic algorithm is an appropriate method to explore the search space of this complex problem and leads to good solutions in a reasonable amount of time.
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