arXiv · cs.AI · Lechen Li (State Key Laboratory of Internet of Things for Smart City, University of Macau, Macau, China, College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, China), Rongye Shi (School of Artificial Intelligence, Beihang University, Beijing, China), Wanhuan Zhou (State Key Laboratory of Internet of Things for Smart City, University of Macau, Macau, China) · 8 小时前模型评分27
DRL-DCO：用深度强化学习动态协调 DE 与 CMA-ES 的进化搜索方法
Learning to Orchestrate Evolutionary Search: Progression-Aware Deep Reinforcement Learning for Dynamic DE-CMA-ES Coordination in Optimization and Structural Model Updating
导读
研究提出 DRL-DCO 算法，用基于深度确定性策略梯度（DDPG，Deep Deterministic Policy Gradient）的演员-评论家智能体统一调度差分进化（DE，Differential Evolution）与 CMA-ES 的搜索过程，动态分配探索与利用算力。
分类置信度
中 · 待人工复核
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来源级别：媒体与个人
应用领域：制造与工业、科学研究
事件类型：研究发布
证据摘录
- “To address this, this study proposes a Deep Reinforcement Learning-governed dynamic DE-CMAES Orchestration (DRL-DCO) algorithm, in which a Deep Deterministic Policy Gradient (DDPG)-based actor-critic agent continuously governs the evolutionary process as a single, unified system rather than a mechanical concatenation of algorithms.”
来源：arXiv · cs.AI · arxiv.org