Reinforcement Learning Assisted Autonomous Selection of Sparsity-Aware Genetic Operators for Sparse Large-Scale Multi-Objective Optimization

Sparse Large-scale Multi-objective Optimization Problems (sparse LMOPs) widely exist in various optimization applications, such as neural network training, portfolio optimization, and feature selection of classification. Although numerous methods exist, automatically selecting efficient solving strategies for sparse LMOPs remains highly challenging. Given this, we propose a reinforcement learning assisted autonomous sparse multi-objective evolutionary algorithm, which aims to effectively utilize sparse knowledge for designing diversified genetic operators, and automatically select appropriate genetic operators for various problems or different situations within the same optimization process. Specifically, three sparsity-aware genetic operators are designed by utilizing sparsity statistic, sparsity clustering, and sparsity logic operation. They possess distinct advantages in terms of convergence speed, solution quality, and diversity. Furthermore, the utilization of deep Q-network enables the automatic selection of suitable operators for offspring reproduction based on the current sparse state of the population. The proposed algorithm is compared with five state-of-the-art algorithms on eight benchmark and three real-world problems. Experimental results demonstrate the superiority of the proposed algorithm and the effectiveness of the proposed sparse genetic operators for solving sparse LMOPs.