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Int. J. Mol. Sci. 2018, 19(1), 62;

Using MOEA with Redistribution and Consensus Branches to Infer Phylogenies

School of Information Science and Technology, Xiamen University, Xiamen 361102, China
National Institute of Diagnostics and Vaccine Development in Infectious Diseases, Xiamen University, Xiamen 361102, China
Author to whom correspondence should be addressed.
Received: 14 November 2017 / Revised: 6 December 2017 / Accepted: 22 December 2017 / Published: 26 December 2017
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In recent years, to infer phylogenies, which are NP-hard problems, more and more research has focused on using metaheuristics. Maximum Parsimony and Maximum Likelihood are two effective ways to conduct inference. Based on these methods, which can also be considered as the optimal criteria for phylogenies, various kinds of multi-objective metaheuristics have been used to reconstruct phylogenies. However, combining these two time-consuming methods results in those multi-objective metaheuristics being slower than a single objective. Therefore, we propose a novel, multi-objective optimization algorithm, MOEA-RC, to accelerate the processes of rebuilding phylogenies using structural information of elites in current populations. We compare MOEA-RC with two representative multi-objective algorithms, MOEA/D and NAGA-II, and a non-consensus version of MOEA-RC on three real-world datasets. The result is, within a given number of iterations, MOEA-RC achieves better solutions than the other algorithms. View Full-Text
Keywords: many-objective optimization; phylogenies; consensus; genetic algorithm many-objective optimization; phylogenies; consensus; genetic algorithm

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Min, X.; Zhang, M.; Yuan, S.; Ge, S.; Liu, X.; Zeng, X.; Xia, N. Using MOEA with Redistribution and Consensus Branches to Infer Phylogenies. Int. J. Mol. Sci. 2018, 19, 62.

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