一种基于GRASP并结合路径重连的模因算法，用于远离大多数字符串问题
A GRASP-based memetic algorithm with path relinking for the far from most string problem
AI总结：
针对计算生物学相关的FFMSP，论文提出结合GRASP初始化、路径重连重组和爬山局部改进的模因算法，并在随机与生物实例上证明其显著优于现有方法。
摘要：
FAR FROM MOST STRING PROBLEM（FFMSP，远离大多数字符串问题）是一个字符串选择问题。其目标是找到一个字符串，使它与某个输入集合中尽可能多的其他字符串的距离高于给定阈值。该问题与计算生物学中的若干任务相关，而且已被证明求解非常困难。我们提出一种模因算法（MA）来处理FFMSP。该MA利用了针对该问题的启发式目标函数，其特点包括：通过Greedy Randomized Adaptive Search Procedure（GRASP，贪婪随机自适应搜索过程）元启发式方法初始化种群，通过路径重连进行密集重组，以及通过爬山法进行局部改进。我们使用随机来源和生物学来源的问题实例进行了广泛的实证评估，以分析参数敏感性并与其他最先进技术进行性能比较。结果表明，该MA的性能优于这些后述技术，且具有统计显著性。
英文摘要：
The FAR FROM MOST STRING PROBLEM (FFMSP) is a string selection problem. The objective is to find a string whose distance to other strings in a certain input set is above a given threshold for as many of those strings as possible. This problem has links with some tasks in computational biology and its resolution has been shown to be very hard. We propose a memetic algorithm (MA) to tackle the FFMSP. This MA exploits a heuristic objective function for the problem and features initialization of the population via a Greedy Randomized Adaptive Search Procedure (GRASP) metaheuristic, intensive recombination via path relinking and local improvement via hill climbing. An extensive empirical evaluation using problem instances of both random and biological origin is done to assess parameter sensitivity and draw performance comparisons with other state-of-the-art techniques. The MA is shown to perform better than these latter techniques with statistical significance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
