田口之參數設計是一公認為具有極大貢獻之品質技術，但是參數設計卻有一些先天之限 制使其使用受限，因此許多學者以類神經網路 (neural networks, NNs) 提出修正模式。然而對 於動態特性之問題，其靈敏度與品質變異常無法同時到達最佳化之目標，且傳統使用之類神 經網路無法保證可以獲得參數與品質特性間可靠的對應關係。本研究提出一演算法，結合階 層式基因演算法 (hierarchical genetic algorithm, HGA) 與多目標演化演算法 (multiple objective evolutionary algorithm, MOEA) 進行動態製程之最佳化程序，以解決上述問題。最後並以一塑 膠射出成型之製程為例，以驗證本方法之有效性。

Many soft computing techniques were used to resolve Taguchi’s parameter design problems. These methods consist of two major steps where neural networks are first adopted to find the functional relationship between the desired responses and control factor values and then simulated annealing or genetic algorithm is applied to determine an optimal combination of control factors. However, neural networks tend to trap the error function in a local minimum when one tries to find the parameters of the network. Besides, the sensitivity measure and variability measure need to be optimized simultaneously in a dynamic system. In this paper, we integrate a hierarchical genetic algorithm (HGA) and a multiple objective evolutionary algorithm (MOEA) to optimize the dynamic parameter design problem. The proposed method applies a HGA based neural network to derive the relationship between the input factors and corresponding outputs, β and SN ratio. Then a MOEA is applied to obtain the non-dominated solution of predicted SN ratio and β. Finally, in the confirmation phase, confirmation experiments are conducted to determine the best parameter setting. An industry case of injection molding process is demonstrated to show the effectiveness and its applicability to other industries.