在生物醫學界，單一核甘酸多型( Single Nucleotide Polymorphism : SNP) 是目前重要的研究議題。為了去了解SNP在基因的功能的影響，常常用Polymerase chain reaction ( PCR) 實驗來放大基因片段來做觀察。為了要讓多條基因在一次的PCR實驗中同時作用放大，引子的設計便格外重要，除了一般目前所知的引子設計條件外，其退化度也不能過高，以達到節省實驗金錢以及時間的花費。觀看目前已知退化型引子設計的軟體還有文獻，還沒有能夠設計出能夠符合所有引子設計條件並同時保持低退化度的引子。我們在這篇使用多目標基因演算法來設計出一個有效率的軟體來找到這樣的引子來使用，並且我們利用容錯的機制，使用投票法來降低引子的退化程度。我們使用了幾個基因組來驗証我們的程式能正確的找出能用的引子，在wet dock實驗中，我們也能確實放大目標基因的片段，証明我們的軟体找到的引子可以適合用在同時放大多條基因的實驗上。

In the medical field, Single Nucleotide Polymorphism (SNP) genotyping is an important genetics technique. In order to know how SNP affect the function of gene, to amplify SNP regions of the DNA sequences is required. Many researchers prefer to design degenerate primers for polymerase chain reaction (PCR) experiment to amplify these sequences. Finding a primer satisfied all PCR design constraints and low degeneracy is important to save time and money. However, the existing applications can’t design the degenerate primer satisfied the degenerate primer design constraints at the same time. We propose an efficient degenerate primer design algorithm based on multiobjective genetic algorithm. Also, the proposed algorithm uses the voting scheme to decrease the degeneracy of the design primers. Several gene family cDNA sequences and sequence variants are used to verify the proposed algorithm. The dry dock experimental results show that the proposed algorithm can find degenerate primer satisfied as many constraints as possible and low degeneracy.

Content
1. Introduction 1
2. Background materials and literature review 4
2.1 Background materials 4
2.1.1 PCR and applications of PCR 4
2.1.2 The degenerate primer design problem 5
2.1.3 Multi-objective genetic algorithm 5
2.2 Literature reviews 9
3. The constraints on degenerate primer design 11
3.1. Definition of the proposed algorithm 11
3.2 The PCR constraints 11
3.3 The degenerate primer constraints 15
4. The proposed algorithm 17
4.1. Problem statement 17
4.2. Finding the conserved region 20
4.3. Initialization 20
4.4. Evaluation 21
4.5. Selection 21
4.6. Crossover 23
4.7. Mutation 24
4.8. Decreasing degeneracy 25
5. Discussion 27
6. Experiments 28
6.2. Wet experiment 33
7. Conclusion 34
REFFERENCES 34

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