The problem of system input selection, dubbed in the literature as Type I Structure Identification problem, is addressed in this paper using an effective novel method. More specifically, the fuzzy curve technique, introduced by Lin and Cunningham (1995), is extended to an advantageous fuzzy surface technique; the latter is used for fast building a coarse model of the system from a subset of the initial candidate inputs. A simple genetic algorithm, enhanced with a local search operator, is used for finding an optimal subset of necessary and sufficient inputs by considering jointly more than one inputs. Extensive simulation results on both artificial data and real world data have demonstrated comparatively the advantages of the proposed method.