Application of Stochastic Optimization Methodology to Bioinformatics -- A Case Study on Applying Ant Colony Optimization to the Shortest Superstring problem

• Main Authors: Pei-Chen Lee, 李佩真
• Other Authors: Chienwen Wu
• Format: Others
• Language: zh-TW
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/8cc5d6

碩士 === 國立臺北科技大學 === 工業工程與管理研究所 === 95 === Bioinformatics has received wide attention in recent years. It is interesting to see how stochastic optimization methodologies such as genetic algorithm, simulated annealing and ant colony optimization, that can be applied to solve problems in bioinformatics. Among many research problems in bioinformatics, the shortest superstring problem has wide applications in many research areas, such as DNA sequencing and data compression. However, the problem is NP-hard and difficult to solve efficiently. In the literature, the ant colony optimization algorithm has been reported to be successfully applied to many combinatorial problems, such as the traveling salesperson problem and the assignment problem. In this paper, we describe the use of the ant colony optimization algorithm to solve the shortest superstring problem, which highlights a way for applying stochastic optimization methodologies to solve problem in bioinformatics.