抄録
Although it is often claimed that due to their probabilistic character genetic algorithm(GA's)are able to avoid getting trapped in local minima, this statement is only valid in a very narrow sense. Especially when it comes to apply GA's for fuzzy model and controller optimization one faces several problems. The reason for this lack of performance lies in the nature of the optimization task itself. For a better understanding of the problem, we first compare the simple genetic algorithm with the simplex downhill optimization method under three different initial conditions. Consequently, we propose an anticipating GA to solve the above mentioned problem. To enhance computing time, this proposed GA is further combined with the downhill simpl exmethod in the final stage of the optimization. Thus, resulting in a hybrid algorithm.
