在自動模糊系統之設計上尋找類神經模糊控制系統(Neural Fuzzy Systems，NFS) 後部值(Consequent values，CV)的組合是一個最佳化的問題（Optimization Problem），已有許多相關研究使用不同技術來求解此最佳化之問題。其中蟻群最佳演算法(Ant Colony Optimization Algorithm，ACO)，在最佳化的解題上具有收斂快速的表現，但此快速收斂容易落入較差的局部區域(Local optima)解中。而求解最佳化問題常用之基因演算法(Genetic Algorithm，GA)，雖然編碼靈活、廣域搜尋能力較佳、較不落入區域解特性、應用廣泛，但它的執行效率上郤較ACO為差，因須花費較多的執行時間來做細部之搜尋。若能將此二演算法混合，各取其優點，將是一個值得研究的問題。
本論文結合基因演算法與蟻群最佳化二演算法提出了一個混合基因演算法與蟻群最佳化類神經模糊控制器之設計法，主要的目的是將基因演算法廣域搜尋與蟻群最佳化的收斂快速和細部搜尋能力之優點相結合，使得當蟻群最佳化演算法在收斂落入較差的區域解時基因演算法可以演化出較好的解來取代較差的區域解，因而在模糊控制器的後部值最佳化之問題上能得到更精準和穏定之解答。
在驗證方面，本論文使用之混合基因演算法與蟻群最佳化在類神經模糊控制系統上，能找出類神經模糊控制系統最佳的後部值之組合。而本論文所提出的演算法和其它演算法做比較，可以驗證本論文所提出的演算法比其它演算法得到之解更精準。

Searching for the combination of consequent values of neural fuzzy systems in automatic fuzzy system design is a Optimization Problem .And there are a lots of solutions method for it. The ant colony optimization algorithm(ACO) has a good performance on convergent but it is easy to get the local optima. In addition, the solution optimization problem uses genetic algorithm (GA)usually because genetic algorithm has a good capability of global searching but it`s performance is lower than ACO. If we can combine GA and ACO to take their advantages it will be a good problem to study.
In this paper, we propose the combination of GA and ACO method. The Objective is to consolidate global search of GA and local search of ACO. The solution from ACO is replaced by the best solution from GA evolution when ACO convergent to local optima, let Consequent values of fuzzy controller be accurate and stable.
This paper uses a hybrid of genetic algorithm and ant colony optimization for fuzzy controller in simulation. We search for problem consequent values of neural fuzzy. we will compare with other different algorithm finally.

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