  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.

Artificial Intelligence