Enhancing Genetic Algorithms with Graph Neural Networks: A Timetabling Case Study

TL;DR

This paper presents a novel hybrid approach combining genetic algorithms and graph neural networks to improve timetabling solutions, demonstrating significant efficiency and quality gains over standalone methods.

Contribution

It introduces the first hybridization of a genetic algorithm with a graph neural network specifically for timetabling optimization, integrating domain knowledge and deep learning.

Findings

Hybrid method outperforms standalone algorithms in solution quality.

Hybrid approach significantly reduces computation time.

Experimental results confirm statistical significance of improvements.

Abstract

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.