Dynamic Graph Neural Evolution: An Evolutionary Framework Integrating Graph Neural Networks with Adaptive Filtering

This paper proposes an innovative optimization framework, Dynamic Graph Neural Evolution (DGNE), integrating Graph Neural Networks (GNNs) with Evolutionary Algorithms (EAs). Building on the foundation of Graph Neural Evolution (GNE), DGNE introduces a dynamic filtering mechanism and adaptive Gaussian sampling functions to dynamically adjust the population distribution during the optimization process, achieving a balance between global exploration and local exploitation. By emphasizing high-frequency information in the early stages to enhance population diversity and low-frequency information in the later stages to promote convergence, DGNE effectively optimizes the search process. Experiments were conducted on the CEC2017 benchmark suite across 30, 50, and 100 dimensions, comparing DGNE with advanced algorithms (LSHADE, LSHADE_cnEpSin, MadDE, SaDE, EA4eig) and classic algorithms (DE and CMA-ES). Statistical analyses using the Wilcoxon rank-sum test and Friedman mean rank test demonstrate that DGNE achieves the best average ranking in 50 and 100 dimensions and ranks third in 30 dimensions. However, it achieves the highest overall average performance ranking across all dimensions, showcasing its stability and significant advantages in different scenarios. While its performance in low-dimensional tasks is slightly less competitive compared to high-dimensional ones, DGNE still exhibits strong competitiveness. Additionally, we explored the impact of population size. DGNE was evaluated across population sizes of 20, 30, 50, and 100. The results highlight DGNE’s robustness, maintaining competitive rankings across all population sizes, with top rankings for smaller population sizes (20 and 30) and strong results at larger sizes (50 and 100). These findings confirm DGNE’s adaptability to varying configurations and further validate its effectiveness as a robust optimization framework. Overall, DGNE demonstrates great potential as an optimization method, offering a promising direction for further research and applications in artificial intelligence and optimization fields. Its ability to remain competitive across diverse tasks and configurations underscores its versatility and scalability.