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

• Published in: Information processing letters Vol. 166; p. 106041
• Main Authors: Djukanovic, Marko, Berger, Christoph, Raidl, Günther R., Blum, Christian
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2021
• Subjects: A⁎ search; Combinatorial problems; Constrained sequences; Longest common subsequences; Constrained sequences; Combinatorial problems; A⁎ search; Longest common subsequences
• ISSN: 0020-0190; 1872-6119; 1872-6119
• DOI: 10.1016/j.ipl.2020.106041

•We introduce an efficient A⁎ search to solve the classical CLCS problem.•A⁎ has been compared to the other state-of-the-art competitors from literature.•Rigorous comparisons were made on artificial and real-world benchmarks.•Experimental studies confirmed that A⁎ is a state-of-the-art algorithm for this problem. 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.