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A Cooperative Co-Evolutionary Genetic Algorithm for Tree Scoring and Ancestral Genome Inference

机译:树木评分与祖先基因组推断的协同进化进化遗传算法

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摘要

Recent advances of technology have made it easy to obtain and compare whole genomes. Rearrangements of genomes through operations such as reversals and transpositions are rare events that enable researchers to reconstruct deep evolutionary history among species. Some of the popular methods need to search a large tree space for the best scored tree, thus it is desirable to have a fast and accurate method that can score a given tree efficiently. During the tree scoring procedure, the genomic structures of internal tree nodes are also provided, which provide important information for inferring ancestral genomes and for modeling the evolutionary processes. However, computing tree scores and ancestral genomes are very difficult and a lot of researchers have to rely on heuristic methods which have various disadvantages. In this paper, we describe the first genetic algorithm for tree scoring and ancestor inference, which uses a fitness function considering co-evolution, adopts different initial seeding methods to initialize the first population pool, and utilizes a sorting-based approach to realize evolution. Our extensive experiments show that compared with other existing algorithms, this new method is more accurate and can infer ancestral genomes that are much closer to the true ancestors.
机译:技术的最新进展使获得和比较整个基因组变得容易。通过逆转和转座等操作进行基因组重排是罕见的事件,使研究人员能够重建物种之间的深层进化历史。一些流行的方法需要在较大的树空间中搜索得分最高的树,因此希望有一种快速而准确的方法可以有效地给定树得分。在树评分过程中,还提供了内部树节点的基因组结构,这为推断祖先基因组和为进化过程建模提供了重要信息。然而,计算树木分数和祖先基因组非常困难,许多研究人员不得不依靠具有各种缺点的启发式方法。在本文中,我们描述了第一种用于树木评分和祖先推断的遗传算法,该算法使用考虑了协同进化的适应度函数,采用不同的初始播种方法初始化第一个种群,并使用基于排序的方法来实现进化。我们广泛的实验表明,与其他现有算法相比,该新方法更准确,并且可以推断出更接近真实祖先的祖先基因组。

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