Speaker: Yuejiao Gong, South China University of Technology, China
Date: 16 December 12 August 2024
Time: 4:00 - 5:00pm (Beijing Time, UTC+8)
Yue-Jiao Gong is a Professor at the School of Computer Science and Engineering, South China University of Technology, China. Her research interests include Optimization Methods based on Swarm Intelligence, Deep Learning, Reinforcement Learning, and their Applications in Smart Cities and Intelligent Transportation. She has published over 100 papers, primarily in ACM/IEEE Transactions series journals and reputable conferences such as GECCO, NeurIPS, and ICLR. Dr. Gong was honored as the Pearl River Young Scholar by the Guangdong Education Department in 2017 and received the Guangdong Natural Science Funds for Distinguished Young Scholars in 2022.
Meta-Black-Box Optimization (MetaBBO) leverages a meta-level learner to automate the black-box optimization process, thereby enhancing efficiency and reducing human intervention. MetaBBO can be categorized into three distinct branches: the first focuses on automatic algorithm configuration or selection; the second employs neural networks to propose candidate solutions directly; and the third generates algorithms, as demonstrated in our study, by producing update rules expressed as closed-form equations. This talk provides a comprehensive overview of these three branches of MetaBBO, highlighting their methodologies, advantages, and current challenges.
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