Optimization of a fuzzy controller by Genetic Algorithms

Manual design of membership functions and rule bases for fuzzy systems often produces nan optimal controllers, both in terms of performance and rule-base complexity. Even algorithms for automatic generation of these two components do generally miss their simultaneous optimal determination, therefore producing fuzzy system with lower performance. This paper addresses the use of a genetic algorithm for the optimization of a working fuzzy controller through the simultaneous tuning of membership functions and fuzzy rules. The parameter coding used by the method does allow the fine tuning of membership functions and, at the same time, the simplification of the rule base by identifying the necessary rules and by selecting the relevant inputs for each of them. Results obtained by applying the method to a fuzzy controller implementing the wall-following task for a real mobile robot will be shown and compared, both in terms of performance and rule base complexity, with those provided by the original non-optimized version.