An Approach Using BRKGA for Optimizing Graph Neural Network Architectures

Graph Neural Networks (GNNs) have emerged as powerful tools for modeling graph-structured data, demonstrating success in node classification, link prediction, and graph classification. However, designing optimal GNN architectures remains a challenging and resource-intensive task due to the complexity of hyperparameter choices and the high-dimensional nature of graph data. Neural Architecture Search (NAS) has been proposed as a solution to automate this process, with evolutionary algorithms showing particular promise in exploring large architectural spaces. This paper introduces the Biased Random-Key Genetic Algorithm for Graph Neural Networks (BRKGA-GNN), a novel framework that applies BRKGA to optimize GNN architectures. BRKGA offers a robust and efficient search mechanism for navigating the architecture space by leveraging biased random keys and evolutionary strategies. To validate the effectiveness of BRKGA-GNN, we conducted experiments on three widely used benchmark datasets, comparing their performance against state-of-the-art NAS-based methods. The proposed methodology provides an automated and scalable solution for designing effective GNN architectures while minimizing reliance on manual tuning. This study highlights the potential of BRKGA-GNN to advance research in both NAS and GNN optimization, inviting further exploration of its applications and benefits.