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An enhanced beam search algorithm for the Shortest Common Supersequence Problem

机译:最短公共超序列问题的一种改进的波束搜索算法

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摘要

The Shortest Common Supersequence Problem asks to obtain a shortest string that is a supersequence of every member of a given set of strings. It has applications, among others, in data compression and oligonucleotide microarray production. The problem is NP-hard, and the existing exact solutions are impractical for large instances. In this paper, a new beam search algorithm is proposed for the problem, which employs a probabilistic heuristic and uses the dominance property to further prune the search space. The proposed algorithm is compared with three recent algorithms proposed for the problem on both random and biological sequences, outperforming them all by quickly providing solutions of higher average quality in all the experimental cases. The Java source and binary files of the proposed IBS_SCS algorithm and our implementation of the DR algorithm and all the random and real datasets used in this paper are freely available upon request.
机译:最短公共超序列问题要求获得最短的字符串,该字符串是给定字符串集中每个成员的超序列。它尤其在数据压缩和寡核苷酸微阵列生产中具有应用。这个问题是NP难题,对于大型实例,现有的精确解决方案是不切实际的。针对该问题,本文提出了一种新的波束搜索算法,该算法采用了一种概率启发式算法,并利用优势属性进一步缩小了搜索空间。将该算法与针对该问题在随机序列和生物学序列上提出的三种最新算法进行了比较,通过在所有实验情况下快速提供更高平均质量的解决方案,使它们的性能均优于其他算法。所提出的IBS_SCS算法的Java源代码和二进制文件以及我们对DR算法的实现以及本文中使用的所有随机和真实数据集均可以根据要求免费获得。

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