Abstract
In view of the problem that the genetic algorithm is easy to fall into local optimization and converge slowly at the later stage of multiple sequence alignment, we propose a multigroup parallel genetic algorithm. We utilize the methods of multigroup parallel and migration strategy, and design a new mutation operator, which enhance its ability to achieve good quality solutions. Then some sequences are chosen from the BALIBASE database 1.0 as test data and the experiment results show the effectiveness of our method.
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Luo, J., Zhang, L., Liang, C. (2011). A Multigroup Parallel Genetic Algorithm for Multiple Sequence Alignment. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds) Artificial Intelligence and Computational Intelligence. AICI 2011. Lecture Notes in Computer Science(), vol 7002. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23881-9_41
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DOI: https://doi.org/10.1007/978-3-642-23881-9_41
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