Embedding-Assisted Genetic Algorithm for Routing Optimization in 6G Networks

As technical discussions surrounding next-generation 6G networks are gaining momentum, dynamic and flexible network operation and management technologies remain essential for supporting services with diverse requirements. Specifically, the emergence and increasing generalization of new communication entities are driving structural changes that demand more dynamic environments and stricter constraints on network operation. These changes render conventional routing optimization significantly more complex, requiring consideration of intricate service characteristics and evolving network conditions. Meanwhile, genetic algorithms (GAs), a class of metaheuristic methods, have been effectively employed for routing optimization. However, the inherent randomness in the initialization of solution populations often leads to limitations in convergence stability and the quality of the final solutions. To address this issue, this paper proposes a routing optimization approach that evaluates the quality of initial solution populations by learning the network state using graph neural networks (GNNs). Based on this prediction, a high-quality initial population is constructed, which serves as the basis for the subsequent execution of the genetic algorithm. The proposed method jointly considers operational costs in both the communication and computational domains, enabling the derivation of optimal paths that satisfy service requirements under the given network conditions.