| Prof. Zhihui ZhanIEEE Fellow Nankai University, China Zhihui Zhan is a Professor and Doctoral Supervisor at the College of Artificial Intelligence, Nankai University. He is an IEEE Fellow and a distinguished recipient of the IEEE Computational Intelligence Society (CIS) Outstanding Early Career Award (globally awarded to one individual annually). His honors also include the Chang Jiang Young Scholar by the Ministry of Education, the National Science Fund for Excellent Young Scholars, the Wu Wenjun Artificial Intelligence Excellent Youth Award, and a Clarivate Global Highly Cited Researcher. He is recognized as a Top 2% World's Most-cited Scientist in artificial intelligence (concurrently featured on both the single-year and career-long impact lists) and has been named a Highly Cited Chinese Researcher for 11 consecutive years from 2014 to 2024.
His primary research encompasses artificial intelligence, evolutionary computation, and swarm intelligence, along with their applications. Prof. Zhan serves as an Associate Editor for four leading IEEE Transactions journals in the fields of evolutionary computation, artificial intelligence, and control: IEEE Transactions on Evolutionary Computation, IEEE Transactions on Emerging Topics in Computational Intelligence, IEEE Transactions on Systems, Man, and Cybernetics: Systems, and IEEE Transactions on Artificial Intelligence.
Speech Title: Data and Knowledge Driven Evolutionary Computation: What to Drive and How to Drive? Abstract: Evolutionary computation (EC) is a kind of powerful artificial intelligence (AI) method for optimization. The EC simulates the evolutionary phenomenon and swarm intelligent behavior in nature, being promising in knowledge creation and problem solving. As the EC algorithms follow the Darwin’s “survival of the fittest” principle to select better solutions and to reproduce new solutions, they may face difficulties when deal with expensive optimization problem if the fitness evaluation is very time/cost consuming or even the fitness function cannot be formulated. The complex optimization problems also challenge the EC algorithms to make them easy to be trapped into local optima or to take too long time to converge to the promising region. Therefore, data-driven EC (DDEC) and knowledge-driven EC (KDEC) have become popular in helping EC algorithms solve these challenging optimization problems. This talk will focus on what to drive in DDEC/KDEC and how to drive the DDEC/KDEC. For what to drive, we focus on building surrogate for fitness evaluation to drive selection and focus on learning successful pattern for help generating solutions to drive evolution. Then in data-driven for selection, we talk about Boosting Data-Driven Evolutionary Algorithm (BDDEA) and Hierarchical and Ensemble Surrogate-assisted Evolutionary Algorithm (HES-EA); in data-driven for evolution, we talk about Learning-aided Evolution for Optimization (LEO) and Knowledge Learning for Evolutionary Computation (KLEC). We hope such new EC paradigms can provide new ways for solving modern ultra-complex optimization problems and promote the new developments of EC and AI.
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 | Prof. Liang HuTongji University, China Dr Liang Hu is a professor with Tongi University and also the Chief Al Scientist with DeepBlue Academy of Sciences, China. His research interests include recommender systems, machine leaming, data science and general intelligence. He has published a number of papers in top rank interational conferences and journals, including WWW, IJCAI, AAAL, ICDM, TOIS, TKDE, TNNLS. He has been invited as the program commitee members of more than 30 top-rank Al interational conferences, including AAAI, IJCAI, ICDM, CIKM, and KDD. He also serves as the reviewer of more than ten top-rank interational journals, including ACM CSUR, IEEE TKDE, ACM TOIS, IEEE TPAMl, etc. In addition,he has presented more than ten tutorials on recommender systems and machine leaing at top-rank Al conferences including IJCAl, AAAl, SIGIR, WWW and ICDM.
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 | Prof. Yuqing SunShandong University, China Sun Yuqing is a Professor at the School of Software, Shandong University. She holds several key professional roles, including Vice Chair of the Collaborative Computing Technical Committee of the China Computer Federation (CCF), Member of the CCF System Software Technical Committee, and Member of the Expert Advisory Committee for E-Government of Shandong Province. She has been a visiting scholar at the University of Hong Kong and Purdue University, USA, and is recognized as a High-Level Innovative Talent at the Shandong Information Communication Technology Institute. Her research focuses on semantic and collaborative computing. In recent years, she has led or participated in over ten national and provincial-level research projects. These include the Major Research Plan of the National Natural Science Foundation of China (NSFC), general projects from the NSFC, the National Key R&D Program of China, the National Sci-Tech Support Plan, projects from the National Development and Reform Commission (NDRC), the Shandong Provincial Key R&D Program, the Shandong Provincial Initiative for Self-Innovation and Achievements Transformation, and the Shandong Provincial Natural Science Foundation. Additionally, she has undertaken numerous industry-sponsored collaborative projects.
Prof. Sun has published more than 80 papers in authoritative domestic and international journals, such as the Chinese Journal of Computers and IEEE Transactions on Dependable and Secure Computing (TDSC), as well as at top-tier international conferences including ICDE and SIGIR. She serves as a reviewer for over twenty academic journals and has been a Program Committee Member for more than ten international academic conferences. Furthermore, she has served as the Program Committee Co-Chair for the National Conference on Computer-Supported Cooperative Work and Social Computing.
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 | Prof. Xiaojun ChangNational High-Level Talent University Of Science And Technology Of China, China Xiaojun Chang, a Chair Professor at the University of Science and Technology of China (USTC), is a recipient of the National High-Level Talent award and the Australian Research Council (ARC) Discovery Early Career Researcher Award (DECRA). His primary research focuses on multimodal learning, computer vision, and green artificial intelligence, with an emphasis on their applications for social good. He has led over ten national-level research projects, including those funded by the ARC Discovery Program and Linkage Program. His research findings have been published in more than 150 papers in top-tier international journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI) and IEEE Transactions on Image Processing (TIP), as well as at top-tier CCF-A conferences. His publications have garnered over 15,000 citations on Google Scholar, with 21 of them recognized as ESI Highly Cited Papers/Hot Papers. He was consistently named a Highly Cited Researcher by Clarivate from 2019 to 2023. Prof. Chang also serves as an Associate Editor for leading international journals including IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), IEEE Transactions on Neural Networks and Learning Systems (TNNLS), and ACM Transactions on Multimedia (TOMM), and as an Area Chair for top-tier CCF-A conferences. |
