发表机构
University of Nova Gorica; Artificial Intelligence Research Institute (IIIA-CSIC); Faculty of Mathematics, University of Belgrade; Jožef Stefan Institute; Institute of Information Sciences (IZUM)（新戈里察大学; 人工智能研究所（IIIA-CSIC）; 贝尔格莱德大学数学学院; 约瑟夫·斯特凡研究所; 信息科学研究所（IZUM））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对可变间隙最长公共子序列问题，提出神经进化启发式算法，结合集成方法优化迭代多源束搜索框架，在合成与真实世界实例上性能优于现有方法。
AI 中文摘要
本研究针对可变间隙最长公共子序列问题（VGLCSP），该问题是经典最长公共子序列问题的变体，带有额外的间隙约束，应用于序列比对和时间序列分析。尽管双序列版本已通过动态规划得到广泛研究，但广义多序列形式通常采用基于束搜索的启发式算法求解，而这些手动设计的算法往往缺乏鲁棒性。为克服这一局限，我们提出一种基于学习的方法，用于自动设计更有效的数据驱动启发式算法。该启发式算法由具有预定义架构的神经网络表示，其权重在神经进化框架内通过遗传算法优化。学习过程在迭代多源束搜索程序（该问题的一种最先进方法）内的权重优化与评估之间交替进行。神经网络并非直接构建解，而是学习指导搜索过程，生成神经进化启发式算法。我们进一步引入一种集成启发式算法，结合学习得到的启发式算法与性能最佳的手动设计启发式算法的得分。将该混合方法集成到迭代多源束搜索框架中后，其在合成基准实例和新引入的具有数据驱动间隙约束的真实世界实例上的表现均优于现有方法。
英文摘要
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 optimized by a genetic algorithm within a neuro-evolutionary framework. The learning process alternates between weight optimization and evaluation within an iterative multi-source beam search procedure, a state-of-the-art method for the problem. Rather than constructing solutions directly, the neural network learns to guide the search process, producing a neuro-evolved heuristic. We further introduce an ensemble heuristic that combines the scores of learned and the best-performing hand-crafted heuristic. Integrated into the iterative multi-source beam search framework, the resulting hybrid approach outperforms existing methods on both synthetic benchmark instances and newly introduced real-world instances with data-driven gap constraints.
Comments15 pages, 4 figures
