通訊系統在傳送過程中，信號會受到頻寬的限制與雜訊的干擾，而產生失真現象。為了減少信號在有限頻寬通道中，受到雜訊與碼際干擾(Intersymbol Interference，ISI)效應的影響，本論文提出一種適用於進化演算法的應用，將進化演算法結合多層感知機架構，運用在4-QAM（Quadrature amplitude modulated）決策迴授等化器（DFE）上來消除雜訊與碼際干擾。
由於多層感知機(Multi-layer perceptron，MLP)其架構具有非線性之特性，可以設計成為良好的通道等化器。但是多層感知機的誤差曲面包含了釵h零梯度點，所以使用複數倒傳遞演算法（Complex backpropagation algorithm，CBP）來訓練多層感知機，常會面臨到陷入局部最小值（Local minimum），而導致無法將多層感知機訓練到最佳。
進化演算法（Evolutionary algorithms，EAs）為一種非梯度坡降學習演算法（non-gradient decent learning algorithm），其根據達爾文『適者生存』的法則，來獲得最佳化的解。我們利用進化演算法具有非梯度坡降搜尋與多點搜尋的技巧，來避免因為初始值位址不佳而無法獲得全域最小值（Global minimum）。
結果顯示，利用進化演算法運算所得到的誤碼率(bit error rate, BER)表現，比用複數倒傳遞演算法還要好，亦比使用傳統最小均方誤差（Least mean-square）決策迴授等化器有更好的效能。

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