隨著網際網路的發達以及電腦設備的進步，再加上寬頻技術的推展，資訊流通的方式從以往傳統的報紙、廣播、電視以及電影等，逐漸轉移到電腦網路上。電腦網路上多元化的資訊呈現方式，包含各種傳播媒介，諸如文字、圖片乃至於聲音、影像等，因此，如何在這樣豐富的資訊中提供使用者所需的資訊是刻不容緩的問題。
　　為了解決上述的問題，就必須做好文件分類的工作，藉由相關知識領域的專家來做文件分類的工作是可以達到不錯的效果，但是畢竟專家的力量是有限，因此自動化文件分類是重要的。
　　本研究結合遺傳演算法（genetic algorithm）與KNN（K-Nearest Neighbors）提出一分類演算法，利用遺傳演算法的特性，篩選訓練文件樣本，剔除對於分類並無幫助的樣本，如此便產生了一訓練模板（pattern）。
　　實證研究方面，針對網路中富含情色資訊之繁體中文網頁實作一分類系統，由實驗數據可得知本研究之分類方法確實改善了未改良之KNN的正確率。

　With the prevalence of Internet, the　improvement of computer architectures, and the development of broadband technology, the channels of information communication have converted from newspapers, broadcasts, TV, and movies to computer networks. Computer networks can transmit information by text, pictures, voices, videos, and so on. How to find what you want in such sophisticated mediums is a tough problem.
　In order to solve this problem, document categorizations is a way. We can classify documents correctly by means of experts’ domain knowledge. But, the efforts that experts can afford are limited, more then it is not time-efficient to classify documents manually. So automatic document categorization is arising.
　This study combined genetic algorithm (a.k.a. GA) and k-nearest neighbors (a.k.a. KNN) to make a novel document categorization algorithm known as GA-based KNN (a.k.a. GKN). First, genetic algorithm was applied to select good training samples that are useful to classify documents, and to discard bad training samples that are useless to classify documents. Then, a pattern was generated. Finally, we applied the pattern generated by GA to classify documents by means of KNN.
　In the evaluation of GKN, I found many materials of pornography on the Internet, and classified them by GKN and KNN separately, compared the accuracy rates of GKN and that of KNN. The study found the effectiveness of GKN was better than KNN indeed.

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