  This work presents a new meta-heuristic approach to model structure selection
of polynomial NARX models. In this respect, the technique penalizes the models
based on the individual contribution of each regressor in representing the
system. The new algorithm is tested on two experimental case studies: the
identification of an electromechanical system and a eletric heater. The results
are compared with Error Reduction Ratio and another meta-heuristic approach.
The proposed method shows its advantages over compared methods in terms of the
trade-off between prediction accuracy and model interpretability. The results
are quantified and compared using the Mean Squared Error (MSE) indices.
