标题：Machine Learning-Based Hybrid Approaches for Combinatorial Optimization

报告时间：2025年2月25日（星期二）9:30-11:00

报告地点：净月大街校区信息科学与技术学院楼324会议室

腾讯会议：295-207-904

主讲人：金燕

报告内容简介：

Recent advances in machine learning and deep learning for combinatorial optimization have significantly impacted the fields of computational intelligence and operational research. In this talk, I will present the challenges faced by traditional algorithms and deep reinforcement learning algorithms in solving combinatorial optimization problems. Then, I will showcase our recent progress in developing hybrid algorithms that leverage machine learning and reinforcement learning techniques. This talk will end by exploring potential future directions for this exciting and rapidly evolving area.

主讲人简介：

Dr. Yan Jin is an associate professor in the School of Computer Science and Technology, Huazhong University of Science and Technology (HUST), China. She received the Ph.D. degree in Computer Science from University of Angers, France, in 2016. She was a StarTrack Visiting Faculty at Microsoft Research Asia in 2021. Her research focuses on the design of effective algorithms for solving combinatorial optimization problems and practical applications, including reinforcement learning, machine learning based search approaches and hybrid evolutionary algorithms. She has published over 30 papers and served as a PC Member for conferences such as IJCAI, AAAI and AAMAS, as well as a reviewer for several well-known journals.