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首页> 外文期刊>Recent patents on DNA & gene sequences >Genetic Algorithms with Permutation Coding for Multiple Sequence Alignment
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Genetic Algorithms with Permutation Coding for Multiple Sequence Alignment

机译：具有置换编码的多序列比对遗传算法

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Multiple sequence alignment (MSA) is one of the topics of bio informatics that has seriously been researched. It is known as NP-complete problem. It is also considered as one of the most important and daunting tasks in computational biology. Concerning this a wide number of heuristic algorithms have been proposed to find optimal alignment. Among these heuristic algorithms are genetic algorithms (GA). The GA has mainly two major weaknesses: it is time consuming and can cause local minima. One of the significant aspects in the GA process in MSA is to maximize the similarities between sequences by adding and shuffling the gaps of Solution Coding (SC). Several ways for SC have been introduced. One of them is the Permutation Coding (PC). We propose a hybrid algorithm based on genetic algorithms (GAs) with a PC and 2-opt algorithm. The PC helps to code the MSA solution which maximizes the gain of resources, reliability and diversity of GA. The use of the PC opens the area by applying all functions over permutations for MSA. Thus, we suggest an algorithm to calculate the scoring function for multiple alignments based on PC, which is used as fitness function. The time complexity of the GA is reduced by using this algorithm. Our GA is implemented with different selections strategies and different crossovers. The probability of crossover and mutation is set as one strategy. Relevant patents have been probed in the topic.
机译：多序列比对（MSA）是已被认真研究的生物信息学的主题之一。这被称为NP完全问题。它也被认为是计算生物学中最重要和最艰巨的任务之一。关于这一点，已经提出了大量启发式算法以找到最佳对准。在这些启发式算法中，有遗传算法（GA）。 GA主要有两个主要缺点：耗时且可能导致局部最小值。 MSA中GA流程的重要方面之一是通过添加和改组Solution Coding（SC）的间隔来最大化序列之间的相似性。介绍了SC的几种方法。其中之一是置换编码（PC）。我们提出了一种基于遗传算法（GA）与PC和2 opt算法的混合算法。 PC有助于对MSA解决方案进行编码，从而最大程度地提高GA的资源，可靠性和多样性。 PC的使用通过将所有功能应用于MSA的排列范围来打开区域。因此，我们建议使用一种算法来计算基于PC的多个比对的评分函数，该算法用作适应度函数。通过使用该算法，可以降低GA的时间复杂度。我们的GA采用不同的选择策略和不同的交叉方式实施。交叉和变异的可能性被设置为一种策略。该主题已探究了相关专利。

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