Genetic algorithm with ant colony optimization (GA-ACO) for multiple sequence alignment |
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| Affiliation: | 1. Computer Engineering and Systems Department, Faculty of Engineering, Zagazig University, Egypt;2. Faculty of Computers and Information, Cairo University, Egypt;3. Departamento de Ciencias Computacionales, Universidad de Guadalajara, Mexico;4. Genetic Department, Faculty of Agriculture, Zagazig University, Egypt;1. Shanghai Key Laboratory of Multidimensional Information Processing, and the Department of Computer Science and Technology, East China Normal University, 3663 North Zhongshan Road, Shanghai 200062, China;2. Electronics and Communication Sciences Unit, Indian Statistical Institute, 203, B. T. Road, Kolkata 700 108, India |
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| Abstract: | Multiple sequence alignment, known as NP-complete problem, is among the most important and challenging tasks in computational biology. For multiple sequence alignment, it is difficult to solve this type of problems directly and always results in exponential complexity. In this paper, we present a novel algorithm of genetic algorithm with ant colony optimization for multiple sequence alignment. The proposed GA-ACO algorithm is to enhance the performance of genetic algorithm (GA) by incorporating local search, ant colony optimization (ACO), for multiple sequence alignment. In the proposed GA-ACO algorithm, genetic algorithm is conducted to provide the diversity of alignments. Thereafter, ant colony optimization is performed to move out of local optima. From simulation results, it is shown that the proposed GA-ACO algorithm has superior performance when compared to other existing algorithms. |
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