Abstract
An efficient approach to solve multiple sequence alignment problem is presented in this paper. This approach is based on parallel genetic algorithm(PGA) that runs on a networked parallel environment. The algorithm optimizes an objective function ‘weighted sums of pairs’ which measures alignment quality. Using isolated independent subpopulations of alignments in a quasi evolutionary manner this approach gradually improves the fitness of the subpopulations as measured by an objective function. This parallel approach is shown to perform better than the sequential approach and an alternative method, clustalw. An investigation of the parameters of the algorithm further confirms the better performance.
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© 1999 Springer-Verlag Berlin Heidelberg
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Anbarasu, L.A., Narayanasamy, P., Sundararajan, V. (1999). Multiple Sequence Alignment Using Parallel Genetic Algorithms. In: McKay, B., Yao, X., Newton, C.S., Kim, JH., Furuhashi, T. (eds) Simulated Evolution and Learning. SEAL 1998. Lecture Notes in Computer Science(), vol 1585. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48873-1_18
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DOI: https://doi.org/10.1007/3-540-48873-1_18
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