模擬類神經網路(Nerous network)的多層感知機(MLP)架構，其非線性的特性結構運用在調適性等化器(Adaptive Equalizer)上，可解決訊號空間的非線性問題。而訊號受符元干擾(ISI)和Noise的影響，等化器的運用是必要的。傳統式調適性等化器利用最小均方差演算法(Least Mean Square, LMS)，而多層感知機的網路學習演算法則為倒傳遞演算法(Backpropagation algorithm, BP)。
本篇論文將探討進化演算法(Evolution algorithms ,EAs)，並將之應用在多層感知機(MLP)之後遞式判別回授等化器(Decision Feedback Equalizer, DFE)上。利用進化演算法全域搜尋(global search)的特性，使等化器的效能達到更理想的狀態。EA是模擬生物基因演進的運用法則，經由交配(crossover)、突變(mutate)、選擇(selection)等程序，找出等化器最佳係數解。此論文並針對各個程序步驟做深入探討，並分析各參數值(parameter)對效能(performance)的影響，更完整建構進化演算法(EA)的設定，使EA能在運用上有更佳的效能。

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