Design of a fuzzy controller using genetic algorithms employing random signal-based learning and simulated annealing

Traditional genetic algorithms, though robust, are generally not the most successful optimization algorithm on any particular domain. Hybridizing a genetic algorithm with other algorithms can produce better performance than both the genetic algorithm and the other algorithms. This paper describes the integration of the genetic algorithm into the random signal-based learning employing simulated annealing which is used as an additional genetic operator in order to get a global solution. The validity of the proposed algorithm is confirmed by applying it to two different examples. One is finding the minimum of the nonlinear function. The other is the optimization of fuzzy control rules to control balance of the inverted pendulum.