Dynamic Algorithm Configuration with Evolution-Aware State Representation for Multi-Objective Combinatorial Optimization

Recently, deep reinforcement learning-based dynamic algorithm configuration (DRL-DAC) has been widely studied in evolutionary computation. However, existing DRL-DAC methods mainly focus on the structure of the current population while neglecting inter-generational evolutionary dynamics, leading to an insufficient characterization of the evolutionary state. To address this limitation, we propose an evolution-aware state representation that incorporates the Kullback–Leibler (KL) divergence between consecutive populations to capture inter-generational transitions. Combined with search-stage information, this metric guides the feature-wise reweighting of node embeddings, providing the RL agent with a more discriminative state representation for accurate parameter adaptation. Extensive experiments on representative multi-objective combinatorial optimization problems demonstrate that our method significantly improves overall optimization performance, achieving superior convergence and diversity compared to state-of-the-art baselines. Furthermore, evaluations under large-scale and many-objective settings highlight its strong generalization capabilities. The source code is available at https://github.com/AetherrehteA/DAC.