2024年10月1日 · The proposed algorithm relies on a diversity-enhanced multi-parent edge assembly crossover to build promising offspring and a reinforcement learning guided variable neighborhood …

2024年7月1日 · Algorithm (5) is a combination of evolutionary algorithm and reinforcement learning. And it considers the transportation time in shop floor scheduling, which has some similarities with the …

2025年8月1日 · To efficiently solve the model, a multi-agent reinforcement learning-aided evolutionary algorithm (MRLEA) is developed. In MRLEA, a grid-based evolutionary framework is introduced to …

2025年8月1日 · In Phase 1, a reinforcement learning-integrated evolutionary algorithm is introduced for search area decomposition, aiming to minimize the area of the grid map exceeding the search area. …

2025年2月1日 · These algorithms use data or experiences generated during the evolutionary process to train machine learning models that assist the evolutionary algorithm in searching for promising …

2023年5月17日 · We propose an efficient evolutionary algorithm based on deep reinforcement learning to solve large-scale SMOPs. Deep reinforcement learning networks are used for mining sparse …

2024年1月22日 · Evolutionary Reinforcement Learning (ERL), which integrates Evolutionary Algorithms (EAs) and Reinforcement Learning (RL) for optimization, has demonstrated remarkable performance …

2025年8月1日 · To solve this problem, a knowledge-guided evolutionary algorithm incorporating reinforcement learning (KEARL) is established to minimize maximum completion time, total energy …

2025年12月1日 · Reinforcement learning adaptively selects intensification operators based on reward feedback, improving exploitation by intensifying promising regions of the search space. An Improved …

Evolutionary reinforcement learning (ERL), which integrates the evolutionary algorithms (EAs) and reinforcement learning (RL) for optimization, has demonstrated remarkable performance …