Speaker: Chao Qian, Associate Professor, Nanjing University, China
Date: 18 September 2023
Time: 8:00 - 9:00pm (New Zealand Time, UTC+13)
Chao Qian is an Associate Professor in the School of Artificial Intelligence, Nanjing University, China. He received the BSc and PhD degrees in the Department of Computer Science and Technology from Nanjing University. After finishing his PhD in 2015, he became an Associate Researcher in the School of Computer Science and Technology, University of Science and Technology of China, until 2019, when he returned to Nanjing University.
His research interests are mainly theoretical analysis of evolutionary algorithms (EAs), design of safe and efficient EAs, evolutionary learning, and application of EAs to solve real-world complex problems. He has published one book “Evolutionary Learning: Advances in Theories and Algorithms”, and over 40 papers in top-tier journals (AIJ, ECJ, TEvC, Algorithmica, TCS) and conferences (AAAI, IJCAI, NeurIPS, ICLR). He has won the ACM GECCO 2011 Best Theory Paper Award, the IDEAL 2016 Best Paper Award, and the IEEE CEC 2021 Best Student Paper Award Nomination. He is an associate editor of IEEE Transactions on Evolutionary Computation, a young associate editor of Science China Information Sciences, an editorial board member of the Memetic Computing journal, and was a guest editor of Theoretical Computer Science. He is a member of IEEE Computational Intelligence Society (CIS) Evolutionary Computation Technical Committee, and was the chair of IEEE CIS Task Force on Theoretical Foundations of Bio-inspired Computation. He has regularly given tutorials and co-chaired special sessions at leading evolutionary computation conferences (CEC, GECCO, PPSN), and has been invited to give an Early Career Spotlight Talk "Towards Theoretically Grounded Evolutionary Learning" at IJCAI 2022. He will be a Program Co-Chair of the 22nd Pacific Rim International Conference on Artificial Intelligence (PRICAI 2025).
Most researchers in the area of evolutionary computation focus on empirical studies. However, theoretical research is definitely very important to this area. In this talk, I will introduce: What are theories of evolutionary algorithms? How can theories help us? Why is theoretical research necessary? Is there any limitation for theoretical research? Finally, I will also give some guidance about how to do theoretical research of evolutionary algorithms.
The Webinar went very successful, with 50+ participants, which is a big number for theory talks.
The slides of the Webinar can be found here.
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