The selection of a particular neural network model belonging to the Pareto front is a problem that exists in all multi-objective algorithms. This paper proposes a novel solution to this problem based on a linear combination of the outputs of the two extremes in the Pareto front, which form an ensemble. The decision support TOPSIS method is used to determine which linear combination creates the best ensemble. This analysis selects the most representative individual that performs better in generalization than the extremes of the Pareto front do.