Neuro-Evolved Heuristics for Variable Gapped Common Subsequence Identification
This study addresses the Variable Gapped Longest Common Subsequence Problem (VGLCSP), a variant of the classical longest common subsequence problem with additional gap constraints and applications in sequence alignment and time-series analysis. While the two-sequence version has been widely studied using dynamic programming, the generalized multi-sequence form is usually solved with beam search-based heuristics, whose hand-crafted designs often lack robustness. To overcome this limitation, we propose a learning-based approach for automatically designing more effective data-driven heuristics. The heuristics are represented by a neural network with predefined architecture, whose weights are...
| Authors | Marko Djukanović, Christian Blum, Aleksandar Kartelj et al. |
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| Primary category | cs.AI |
| Published | 1 August 2026 |
| Last updated at source | 1 August 2026 |
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