An A⁎ search algorithm for the constrained longest common subsequence problem

Abstract

The constrained longest common subsequence (CLCS) problem was introduced as a specific measure of similarity between molecules. It is a special case of the constrained sequence alignment problem and of the longest common subsequence (LCS) problem, which are both well-studied problems in the scientific literature. Finding similarities between sequences plays an important role in the fields of molecular biology, gene recognition, pattern matching, text analysis, and voice recognition, among others. The CLCS problem in particular represents an interesting measure of similarity for molecules that have a putative structure in common. This paper proposes an exact A⁎ search algorithm for effectively solving the CLCS problem. This A⁎ search is guided by a tight upper bound calculation for the cost-to-go for the LCS problem. Our computational study shows that on various artificial and real benchmark sets this algorithm scales better with growing instance size and requires significantly less computation time to prove optimality than earlier state-of-the-art approaches from the literature.