Fuzzy rule bases provide a tool for modeling complex systems and approximating functions. Originally, heuristic analysis by experts was used to produce fuzzy models. Recently, algorithms have been developed to produce models from training data. In this research, two general approaches for evolutionary generation of fuzzy rules are identified and compared: global and local reproduction. Global reproduction, which is the standard approach, considers an entire rule base in performing fitness evaluation and regeneration. The local approach considers a series of independent evolutionary selections and produces a model by combining the localized results. An experimental suite has been developed to compare the effectiveness of the approaches in generating models. The parameters considered include the size office training set and the number of rules.