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OBF (orthonormal basis function) fuzzy models have shown to be a promising approach to the areas of nonlinear system identification and control since they exhibit several advantages over those dynamic model topologies usually adopted in the literature. A more general architecture, called generalized OBF Takagi-Sugeno fuzzy model, was introduced in previous work and provided the mathematical interpretation that was missing to the former OBF fuzzy models. In spite of its clear mathematical meaning, however, the identification of this new generalized model is not a trivial task. This paper discusses the use of a genetic algorithm (GA) especially designed for this task, where a fitness function based on the Akaike information criterion plays a key role by considering both model accuracy and parsimony aspects. The hybridization of the GA with classical estimation algorithms is also investigated. Specifically, two different hybridization approaches (with global and local least squares) are evaluated in the modeling of a real nonlinear magnetic levitation system.