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.

FAR FROM MOST STRING PROBLEM (FFMSP) 的 MEMETIC 算法在计算生物学中的字符串选择问题上表现优异且与其他先进技术有统计显著性的性能比较。