| Resumo: | In the system identification context, neural networks are black-box models, meaning that both their parameters and structure need to be determined from data. Their identification is often done iteratively in an ad-hoc fashion focusing the first aspect. Frequently the selection of inputs, model structure, and model order are underlooked subjects by practitioners, because the number of possibilities is commonly huge, thus leaving the designer at the hands of the curse of dimensionality. Moreover, the design criteria may include multiple conflicting objectives, which gives to the model identification problem a multiobjective combinatorial optimisation character. Evolutionary multiobjective optimisation algorithms are particularly well suited to address this problem because they can evolve optimised model structures that meet pre-specified design criteria in acceptable computing time. In this article the subject is reviewed, the authors present their approach to the problem in the context of identifying neural network models for time-series prediction and for classification purposes, and two application case studies are described, one in each of these fields.
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