Abstract
We describe in this paper the use of hierarchical genetic algorithms (HGA) for fuzzy system optimization in intelligent control. In particular, we consider the problem of optimizing the number of rules and membership functions using an evolutionary approach. The HGA enables the optimization of the fuzzy system design for a particular application. We illustrate the approach with two cases of intelligent control. Simulation results for both applications show that we are able to find an optimal set of rules and membership functions for the fuzzy control system.