人類的基本組成物質為去氧核醣核酸（DNA），但最主要的作用由蛋白質完成。就單一人類而言，DNA的序列對（基因組大小）為30億組，從如此大量的資料中我們可以找出某些序列之間的關係。利用序列排比（Sequence Alignment）尤其是多重序列排比，可以幫助我們去預測新序列的二維或三維架構，且找出它們之間的關係。多重序列排比在生物資訊學中是一個很重要也具有挑戰性的問題。在這篇論文中，我們使用了遺傳演算法，再加上動態編程法（DP，Dynamic Programming）一起作用來找出最佳的多重排比。遺傳演傳法在複雜的問題領域上是一項很強的工具，我們主要利用它找出固定合理的配合區塊（Match Block），然後利用DP來處理中間非配合區域（Mismatch Area）。最後實驗數項資料組，我們發現這種方法是可行。 Multiple sequence alignment (MSA) is an important and challenging problem in computational biology. Using sequence alignment skill, especially MSA (multiple sequence alignment) we may extract the function of genes, to help predict the secondary or tertiary structure of new sequences, and to find the relationship between sequences. In this thesis, we combine genetic algorithms (GA) and dynamic programming (DP) together to find protein alignment. Genetic algorithm is a strong stochastic approach for efficient and robust search in large space and time area. We use a genetic approach to find reasonable match blocks and isolate them. We then apply modified dynamic programming to do pairwise alignment in each mismatch blocks. We apply our approach to several data sets, and from the experimental results we find our approach is promising.
