在PCR實驗中，要能成功大量複製目的DNA序列，需有合適的前後引子，依據PCR的實驗特性，引導出找尋引子的一些重要限制條件。本論文中，為降低大量的搜尋空間並提高引子品質，利用雙直交表智慧衍交型基因演算法去處理引子設計。基礎於基因演算法之雙直交表智慧衍交型基因演算法，將品質控制工程中講求精英政策的田口法，融入智慧型衍交之子系統中，透過其直交表的實驗配置系統化大量減少實驗次數，而達成快速收斂的智慧型基因世代演進效果。經實驗顯示雙直交表分別進化前後引子能有快速收斂之效，輸出結果相較於現行其他網路上引子設計程式，能有更符合PCR實驗特性之可用引子解，並在PCR實驗驗證可獲得正確之目的DNA。

In polymerase chain reaction (PCR), in order to amplify massive DNA sequences successfully, it needs to design an appropriate primer pair. The constraints derived from the traits of PCR for proceeding PCR are used in searching for primer pairs. In this paper, in order to decrease the searching space and to increase the feasible quality of primers, a double orthogonal arrays intelligent crossover genetic algorithm (DOAIGA) is used to solve the primer design problem. DOAIGA combines the traditional genetic algorithm and the Taguchi methodology to efficiently search feasible primers under required constraints. The proposed intelligent crossover subsystem mainly concentrates on the better genes more systematic. The key point of DOAIGA is to achieve the elitism goal by applying the orthogonal arrays (OAs) that is used in quality engineering with a small amount of experiment features. In this thesis, the double orthogonal arrays are used to approach a better forward and reverse primers separately. Compared to the current existing softwares, DOAIGA can obtain feasible primer pairs more effectively. Finally the correctness of primer pair is verified by PCR experiment.

Chapter 1. Introduction 1
Chapter 2. Background Materials 3
2.1 Constrains of PCR 3
2.2 Genetic Algorithm 4
2.3 Taguchi Method in Quality Engineering 5
2.4 The Orthogonal Arrays 7
2.5 Literature Reviews 9
Chapter 3. The Proposed Algorithm 12
3.1 The Proposed Intelligent Crossover Subsystem Definitions 12
3.1.1 Initial 12
3.1.2 Selection 13
3.1.3 Intelligent Crossover 13
3.1.4 Mutation and Other Operations 14
3.2 The Double Orthogonal Arrays Intelligent Crossover Genetic Algorithm 14
3.2.1 Length of DNA Sequence, target DNA and Primers 15
3.2.2 Melting Temperature and GC Proportion 16
3.2.3 Annealing 17
3.2.4 Specificity Test 20
3.2.5 Fitness Evaluation 20
3.2.6 Chromosome 22
3.2.7 Mating Pairs 22
3.2.8 Application of Orthogonal Arrays 24
3.3 Algorithm Complexity Analysis and Implementation 27

Chapter 4. Experimental Results 29
4.1 Fitness Evaluating Adjustment 29
4.2 Performance Analysis 30
4.3 Agorose Gel Eletrophoresis 33

Chapter 5. Conclusions 35
References 36

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