  This work presents a new meta-heuristic approach to select the structure of
polynomial NARX models for regression and classification problems. The method
takes into account the complexity of the model and the contribution of each
term to build parsimonious models by proposing a new cost function formulation.
The robustness of the new algorithm is tested on several simulated and
experimental system with different nonlinear characteristics. The obtained
results show that the proposed algorithm is capable of identifying the correct
model, for cases where the proper model structure is known, and determine
parsimonious models for experimental data even for those systems for which
traditional and contemporary methods habitually fails. The new algorithm is
validated over classical methods such as the FROLS and recent randomized
approaches.
