This paper presents a new method for optimizing both the shape and the internal parameters of fuzzy membership functions. Given a specific shape of membership functions, the corresponding internal parameters are optimized by a genetic algorithm. The overall optimization of membership functions is done using a learning automaton coupled with several genetic algorithms each running in its own parameters space. At each step, this learning automaton permits to randomly select the next parameters space according to the behavior evaluation of the current genetic algorithm.
Fuzzy Logic Controllers, Optimization, Genetic Algorithm, Learning Automaton.
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