本論文提出了一個結合改良型的DNA概念和人工蜂群演算法並結合共生概念的混合改良型DNA人工蜂群優化算法來幫助階層式模糊分類系統進行分類。另外，本論文建構了一個結合標準模糊推理系統和DNA編碼與監督式學習的多變數編碼的階層式模糊分類模型來處理分類問題。透過基於共生的混合改良型DNA人工蜂群優化演算法獲取隸屬函數的數量和形狀進而提供階層式模糊分類系統充足的全域性探索及區域性利用。傳統上，人工蜂群演算法通常運用於解決約束和非約束的問題，並非像本論文所提的共生為基本的混合改良型DNA人工蜂群優化算法來協助階層式模糊分類系統進行分類。另一多變數編碼的階層式模糊分類模型將分成四個階段實施：首先，利用基因遺傳演算法決定各模糊子集合中各特徵萃取單元的組合。第二，透過DNA編碼計算來調整隸屬函數的形狀及分佈。第三，混沌粒子群優化演算法用於調整推論單位的主要輸出節點，即模糊法則的權重。最後，以多目標優化功能確保獲得最佳分類率與最小數目和長度的模糊法則。我們採用了五個具代表性的分類資料庫，包含UCI Pima Indians Diabetes, Glass, Wisconsin Breast Cancer, Wine, and Iris databases來驗證本文所提出的基於共生的混合改良型DNA人工蜂群優化算法以及多變數編碼的階層式模糊分類模型的性能。

This dissertation proposes a symbiosis based hybrid modified DNA-ABC optimization algorithm which combines modified DNA concepts and artificial bee colony (ABC) algorithm to aid hierarchical fuzzy classification. The partition number and the shape of the membership function are extracted by the symbiosis based modified DNA- ABC optimization algorithm, which provides both sufficient global exploration and also adequate local exploitation for hierarchical fuzzy classification. According to literature, the ABC algorithm is traditionally applied to constrained and unconstrained optimization problems, but is combined with modified DNA concepts and implemented for fuzzy classification in this present research. This dissertation also constructs the new variable coded hierarchical fuzzy models (VCHFM) synergistically integrates the standard fuzzy inference system and DNA coding with supervised learning to deal with the classification problems. There are four stages to implement this model. First, a genetic algorithm (GA) procedure is used to determine the distribution of fuzzy sets for each feature variable of the feature extraction unit. Second, the membership functions are adjusted by DNA computing. Third, the chaotic particle swarm optimization (CPSO) is used to regulate the weight grade of the principal output node of the inference unit. Finally, a multi-objective optimum fitness function is used to ensure the best classification rate with the minimum number and length of rules. The proposed symbiosis based hybrid modified DNA-ABC optimization algorithm and VCHFM is validated through classifying the five benchmark databases: the UCI Pima Indians Diabetes, Glass, Wisconsin Breast Cancer, Wine, and Iris databases.

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