分類演算法在面對高維度大量的資料運算，通常會造成運算效率缺乏。因此本研究設計一個解析編碼之演化式分類演算法，演算法具備兩大特色，第一個特色是以解析方格的方式，解析每一個資料空間內的資料，這種方法可以處理資料點分佈複雜的問題；第二個特色為透過演化式反覆計算機制，求得較適合分類模型。為進一步提升解析編碼之演化式分類演算法的運算效率，因此本研究加入多核心程式設計方法，以多核心處理器加速解析編碼之演化式分類演算法的效率。本研究的開發了一般單核心程式的版本，與支援平行化計算的多核心程式版本，並比較單核心版本與多核心版本的效能，及分析各種不同平行化的機制間加速的效果。

This study presents GAREC (genetic algorithms with resolution-encoded chromosome) for classification. GAREC partitions the data space into resolution lattices which are encoded as chromosomes to be processed by genetic algorithms (GA.) A resolution lattice is a decision region of a class which is determined by GA. A lattice containing two or more data points belonging to different classes is considered as a new classification problem to be processed by GAREC iteratively. From the experimental results, GAREC is effective to the classification problems. In order to improve the performance of GAREC, this study implements GAREC on a multi-core computing environment using Open-MP. The performance of single- and multi-core versions of GAREC is evaluated and compared.

第一章 緖論
1.1 研究背景與目的
1.2 研究架構
第二章 文獻探討
2.1 分類演算法
2.2 演化式計算
2.3 平行與分散式處理
2.3.1 平行處理
2.3.2 格網運算
2.4 平行化資料分類
第三章 解析編碼之演化式分類演算法
3.1解析式染色體編碼
3.2 解析編碼之演化式分類演算法
3.3 演化式分類演算法運算元機制
3.3.2 適應函數
3.3.3 挑選機制
3.3.4交配與突變機制
3.3.5重生(Restart)機制
3.6 演算法流程圖及步驟說明
3.7 建立分類模型
3.8 實驗分類效果
第四章 多核心程式設計與方法
4.1多執行緖程式設計
4.1.1執行緖
4.1.2多執行緖程式設計方法
4.2平行化策略
4.2.1資料分割(Data Decomposition)
4.2.2.任務分割(Task Decomposition)
4.3演化式分類程式設計階段
4.4平行化階段
第五章 整合後實驗與不同參數分析
5.1實驗測試規劃
5.2實驗測試環境
5.3 實驗參數
5.4 多核心效率實驗
第六章 結論與討論
6.1 結論
6.2 討論

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