Fuzzy rule bases have proven to be an effective tool for modeling complex systems and approximating functions. The generation of a fuzzy rule base has generally been accomplished by a heuristic analysis of the relationships of the underlying system or by algorithmic rule generation from training data. Automatic rule generation has utilized clustering algorithms, proximity analysis, and evolutionary techniques to identify approximate relationships between the input and the output. In this research, two general approaches for the evolutionary generation of fuzzy rules are identified and compared: global and local rule generation. Global rule production, which is the standard method of employing evolutionary techniques in fuzzy rule base generation, considers an entire rule base as an element of population. The fitness evaluation of a rule base aggregates the performance of the model over the entire space into a single value. The local approach utilizes the limited scope of a fuzzy rule to evaluate performance in regions of the input space. The local generation of rule bases employs an independent evolutionary search in each region and combines the local results to produce a global model. An experimental suite has been developed to compare the effectiveness of the two strategies for the evolutionary generation of fuzzy models.