An evolutionary approach for tuning parametric Esau and Williams heuristics

Owing to its inherent difficulty, many heuristic solution methods have been proposed for the capacitated minimum spanning tree problem. On the basis of recent developments, it is clear that the best metaheuristic implementations outperform classical heuristics. Unfortunately, they require long computing times and may not be very easy to implement, which explains the popularity of the Esau and Williams heuristic in practice, and the motivation behind its enhancements. Some of these enhancements involve parameters and their accuracy becomes nearly competitive with the best metaheuristics when they are tuned properly, which is usually done using a grid search within given search intervals for the parameters. In this work, we propose a genetic algorithm parameter setting procedure. Computational results show that the new method is even more accurate than an enumerative approach, and much more efficient.