论文标题
最长的最长常见子序列解决方案服务中的最长常见底带：一种新型超增强效应
Longest Common Substring in Longest Common Subsequence's Solution Service: A Novel Hyper-Heuristic
论文作者
论文摘要
最长的常见子序列（LCS）是在一组字符串之间找到子序列的问题，这些字符串具有两个对所有人共有的特性，并且是最长的。 LCS在计算生物学和文本编辑中都有应用。由于最长的常见子序列的NP硬度，已经提出了许多启发式算法和求解器为不同的字符串提供最佳解决方案。他们都没有所有类型的集合的最佳性能。另外，没有方法可以指定给定的一组字符组的类型。除此之外，可用的超高式效率不足以且速度足够快，无法在现实世界中解决此问题。本文提出了一种新型的超女性，以使用新的标准解决最长的常见子序列问题，以根据其相似性对一组字符进行分类。为此，我们提供了一个一般的随机框架，以识别给定的一组字符组的类型。在此之后，我们基于将集合类型分为两者的框架，介绍了集合相似性DiChotomizer（$ s^2d $）算法。该算法是在本文中首次引入的，并开辟了一种超越当前LCS求解器的新方法。然后，我们提出了一种小说的超级女性，它利用了$ s^2d $和集合的内部属性之一，以选择一组启发式方法中最好的匹配启发式。我们将基准数据集上的结果与最佳的启发式方法和超高术进行了比较。结果表明，在解决方案的质量和运行时间因素方面，我们提出的超高热疗法的性能更高。
The Longest Common Subsequence (LCS) is the problem of finding a subsequence among a set of strings that has two properties of being common to all and is the longest. The LCS has applications in computational biology and text editing, among many others. Due to the NP-hardness of the general longest common subsequence, numerous heuristic algorithms and solvers have been proposed to give the best possible solution for different sets of strings. None of them has the best performance for all types of sets. In addition, there is no method to specify the type of a given set of strings. Besides that, the available hyper-heuristic is not efficient and fast enough to solve this problem in real-world applications. This paper proposes a novel hyper-heuristic to solve the longest common subsequence problem using a novel criterion to classify a set of strings based on their similarity. To do this, we offer a general stochastic framework to identify the type of a given set of strings. Following that, we introduce the set similarity dichotomizer ($S^2D$) algorithm based on the framework that divides the type of sets into two. This algorithm is introduced for the first time in this paper and opens a new way to go beyond the current LCS solvers. Then, we present a novel hyper-heuristic that exploits the $S^2D$ and one of the internal properties of the set to choose the best matching heuristic among a set of heuristics. We compare the results on benchmark datasets with the best heuristics and hyper-heuristics. The results show a higher performance of our proposed hyper-heuristic in both quality of solutions and run time factors.
