This paper investigates the impact of hybridizing a multi-modal Genetic Algorithm with a Graph Neural Network for timetabling optimization. The Graph Neural Network is designed to encapsulate general domain knowledge to improve schedule quality, while the Genetic Algorithm explores different regions of the search space and integrates the deep learning model as an enhancement operator to guide the solution search towards optimality. Initially, both components of the hybrid technique were designed, developed, and optimized independently to solve the tackled task. Multiple experiments were conducted on Staff Rostering, a well-known timetabling problem, to compare the proposed hybridization with the standalone optimized versions of the Genetic Algorithm and Graph Neural Network. The experimental results demonstrate that the proposed hybridization brings statistically significant improvements in both the time efficiency and solution quality metrics, compared to the standalone methods. To the best of our knowledge, this work proposes the first hybridization of a Genetic Algorithm with a Graph Neural Network for solving timetabling problems.

翻译：本文研究了将多模态遗传算法与图神经网络进行混合以优化时间表编排的效果。该图神经网络旨在封装通用领域知识以提升调度质量，而遗传算法则负责探索搜索空间的不同区域，并将深度学习模型作为增强算子集成以引导解搜索趋向最优。首先，该混合技术的两个组成部分均经过独立设计、开发与优化，以解决目标任务。针对人员排班这一经典时间表编排问题，我们进行了多组实验，将所提出的混合方法与遗传算法和图神经网络的独立优化版本进行比较。实验结果表明，相较于独立方法，所提出的混合方法在时间效率和解质量指标上均带来统计显著的提升。据我们所知，本研究首次提出了将遗传算法与图神经网络混合用于解决时间表编排问题。