本文提出一種新的進化演算法混合沌混變數的搜尋方法，命名為混沌進化演算法。由於混沌變數具有虛擬隨機、遍歷性和不規則的特性，使得混沌進化演算法的子代族群可以遍歷性的分佈在搜尋的空間且可較快收斂到最佳解。然後我們利用混沌進化演算法去找到混合參數變動飽和系統的雙速率觀測基底追蹤器。混沌進化演算法可以更容易跳出局部極大或極小值，而找到全域的極大或極小值。最後，以一個例子來驗證這個方法的有效性。

A novel evolutionary-programming (EP) algorithm including chaotic variable named chaos-evolutionary-programming algorithm (CEPA) has been proposed in this thesis. Due to the nature of chaotic variable, i.e. pseudo-randomness, ergodicity and irregularity, the evolutional process of CEPA makes the individuals of subgenerations distributed ergodically in the defined space and circumvents the premature of the individuals of subgenerations. Then a dual-rate observer-based tracker for a hybrid interval chaotic system with saturating actuators by using the CEPA is developed. The CEPA can search many local minimum or maximum in parallel and thereby increasing the probability of finding the global one. An illustrative example is presented to demonstrate the effectiveness of the proposed algorithm.

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