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An improved heuristic algorithm for founder sequence reconstruction from SNP recombinants

机译:从SNP重组子重建创始人序列的改进启发式算法。

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The maximum fragment length problem (MFL) is an effective mathematical model for solving the founder sequence reconstruction problem. Roli et al. proposed a constructive heuristic algorithm (it is named as RHRC in this paper) for solving the MFL model and a tabu search method for further optimizing the solution obtained by RHRC. RHRC algorithm produces uncertain solutions by introducing stochastic information. In this paper, an improved algorithm I-RHRC is presented with the explicit aim of providing ascertained solutions. Instead of using stochastic values, I-RHRC takes advantage of some potential information, i.e. the proportion of 0 and 1 entries in the column of the founder matrix and that of the recombinant matrix, and some other heuristic information, to get ascertained values. Experimental results show not only that I-RHRC algorithm can get better solutions than RHRC algorithm, but also that the tabu search method based on I-RHRC outperforms that based on RHRC.
机译:最大片段长度问题(MFL)是解决创始人序列重建问题的有效数学模型。 Roli等。提出了一种构造性启发式算法(本文称为RHRC)来求解MFL模型,并提出了一种禁忌搜索方法来进一步优化RHRC获得的解。 RHRC算法通过引入随机信息产生不确定的解决方案。在本文中,提出了一种改进的算法I-RHRC,其明确目标是提供确定的解决方案。 I-RHRC并非使用随机值,而是利用了一些潜在的信息,即在创建者矩阵和重组矩阵的列中的0和1条目的比例以及一些其他启发式信息来获得确定的值。实验结果表明,不仅I-RHRC算法比RHRC算法具有更好的解法,而且基于I-RHRC的禁忌搜索方法优于基于RHRC的禁忌搜索方法。

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