A learning search algorithm for the Restricted Longest Common Subsequence problem

• Published in: Expert systems with applications Vol. 284; p. 127731
• Main Authors: Djukanović, Marko, Reixach, Jaume, Nikolikj, Ana, Eftimov, Tome, Kartelj, Aleksandar, Blum, Christian
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 25.07.2025
• Subjects: A search; Beam search; Learning heuristics; Longest Common Subsequence problem; Neural networks; Longest Common Subsequence problem; A search; Neural networks; Learning heuristics; Beam search
• ISSN: 0957-4174
• DOI: 10.1016/j.eswa.2025.127731

This paper addresses the Restricted Longest Common Subsequence (RLCS) problem, an extension of the well-known Longest Common Subsequence (LCS) problem. This problem has significant applications in bioinformatics, particularly for identifying similarities and discovering mutual patterns and important motifs among DNA, RNA, and protein sequences. Building on recent advancements in solving this problem through a general search framework, this paper introduces two novel heuristic approaches designed to enhance the search process by steering it towards promising regions in the search space. The first heuristic employs a probabilistic model to evaluate partial solutions during the search process. The second heuristic is based on a neural network model trained offline using a genetic algorithm. A key aspect of this approach is extracting problem-specific features of partial solutions and the complete problem instance. An effective hybrid method, referred to as the learning beam search, is developed by combining the trained neural network model with a beam search framework. An important contribution of this paper is found in the generation of real-world instances where scientific abstracts serve as input strings, and a set of frequently occurring academic words from the literature are used as restricted patterns. Comprehensive experimental evaluations demonstrate the effectiveness of the proposed approaches in solving the RLCS problem. Finally, an empirical explainability analysis is applied to the obtained results. In this way, key feature combinations and their respective contributions to the success or failure of the algorithms across different problem types are identified. •A new learning-based beam search is proposed to tackle the RLCS problem.•Designed both instance-specific and global features of the RLCS instances.•These features served to train multilayer perceptron network in an offline mode.•Outcome of the trained network used to design prominent heuristic guidance.•State-of-the art results obtained by the learning algorithm on both benchmark sets.