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Single- and Multi-objective phylogenetic analysis of primate evolution using a genetic algorithm

机译:使用遗传算法对灵长类动物进化的单目标和多目标系统发育分析

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Starting with the protein-coding mitochondrial DNA sequences of 20 different species, we reconstruct the primate evolutionary tree using maximum likelihood fitness functions based on a general Markov model of evolution. There is evidence that first and second codon sites in this DNA evolve under different conditions. Thus, we used a combination of a genetic algorithm (GA) and both single and multi-objective optimisation (MOO) to search tree-space for optimal solutions. Various genetic operators were used to search the combinatorial space of evolutionary trees, and a Pareto set was obtained. The implications of the common evolutionary subtrees to all trees found on the Pareto set are that the first codon sites play a far more important role in determining the optimal tree for these data. In the present case, the evolutionary relationship among the simian and other primates considered here remains in question.
机译:从20种不同物种的蛋白质编码线粒体DNA序列开始,我们基于一般的马尔可夫进化模型,使用最大似然适应度函数重建灵长类动物进化树。有证据表明该DNA中的第一个和第二个密码子位点在不同条件下进化。因此,我们结合使用了遗传算法(GA)和单目标与多目标优化(MOO)来搜索树空间以获得最佳解决方案。使用各种遗传算子搜索进化树的组合空间,并获得帕累托集。共同的进化子树对在Pareto集上发现的所有树的含义是,第一个密码子位点在确定这些数据的最佳树时起着更为重要的作用。在目前的情况下,这里所考虑的猿猴和其他灵长类动物之间的进化关系仍然存在疑问。

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