Name：
Yue-Jiao Gong
Professional Title / Position：
Professor
Research Direction：
Computational Intelligence, Large Language Models, Autonomous Decision Systems
Team：
MetaEvo
Tel：/
Email：
gongyj@scut.edu.cn
Biography：
Yue-Jiao Gong received the B.S. and Ph.D. degrees in Computer Science from Sun Yat-sen University, China, in 2010 and 2014, respectively. She is currently a Full 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 reinforcement learning, and large language models, alongside their practical applications in autonomous decision systems. She has published over 100 papers, including more than 60 in ACM/IEEE Transactions and over 50 at renowned conferences such as NeurIPS, ICLR, and GECCO. Dr. Gong was awarded the Pearl River Young Scholar by the Guangdong Education Department in 2017 and the Guangdong Natural Science Funds for Distinguished Young Scholars in 2022. She was named to the Stanford World’s Top 2% Scientists list in the artificial intelligence field. She is currently an Associate Editor of IEEE Transactions on Evolutionary Computation and ACM Transactions on Evolutionary Learning and Optimization. She has contributed as an Area Chair or Senior PC member for several leading conferences in her field.
Education：
2010 –2014School of Information Science and Technology, Sun Yat-sen University
2006 – 2010School of Information Science and Technology, Sun Yat-sen University
Work Experience：
2019– presentSchool of Computer Science and Technology, South China University of Technology, Professor
2017 – 2018School of Computer Science and Technology, South China University of Technology, Associate Professor
2015 – 2016Department of Computer and Information Science, University of Macau, Post-Doctoral Research Fellow
2014 - 2014Fok Ying Tung Graduate School, Hong Kong University of Science and Technology, Research Assistant
Course：
Intelligent Algorithms and Applications
Reinforcement Learning and Intelligent Decision
Data Warehouse and Data Mining
Foundations of Computer Science
Projects：
1. 2023 – 2026Neural Evolutionary Algorithms with Autonomous Operation and Memory Mechanism, National Natural Science Foundation of China (NSFC) General Project, PI
2. 2019 – 2022Swarm Intelligence Algorithms for Large-Scale Optimization and Their Applications to Area-Level Coordinated Control of Traffic Signals, National Natural Science Foundation of China (NSFC) General Project, PI
3. 2018 – 2021The New Generation of Computational Intelligence: Theory and Applications, National Natural Science Foundation of China (NSFC) – Guangdong Union Key Project, Co-PI
4. 2016 – 2018Cooperative Coevolution-Based Estimation of Distribution Algorithm for Large-Scale Transportation Scheduling, National Natural Science Foundation of China (NSFC) Youth Project, PI
5. 2022 – 2025Data-Driven Swarm Intelligence Algorithms and Applications, Guangdong Natural Science Funds for Distinguished Young Scholars, PI
6. 2021 - 2024Cooperative Coevolutionary Framework for Decentralized Large-Scale Deep Learning Models, Guangdong Basic and Applied Basic Research Regional Joint Fund Key Project, Co-PI
7. 2025 - 2027Natural Language Representation and Automatic Customization of Evolutionary Algorithms, Guangzhou Basic and Applied Research Project for Leading Talent Scholars, PI
8. 2019 – 2022Coordinated Signal Timing Using Computational Intelligence, Guangzhou Science and Technology Planning Project, PI
Publications：
1. Y.-T. Zhong, T. Huang, X. Xiao, Y.-J. Gong*, “TRACE: A Generalizable Drift Detector for Streaming Data-Driven Optimization,” The 39th AAAI Conference on Artificial Intelligence (AAAI), 2026.
2. Z. Ma, Y.-J. Gong*, et al., “MetaBox-v2: A Unified Benchmark Platform for Meta-Black-Box Optimization,” Advances in Neural Information Processing Systems (NeurIPS), 2025.
3. H. Guo, Z. Ma, Y. Ma, X. Zhang, W.-N. Chen, Y.-J. Gong*, “DesignX: Human-Competitive Algorithm Designer for Black-Box Optimization,” Advances in Neural Information Processing Systems (NeurIPS), 2025.
4. Z. Ma, Y.-J. Gong*, et al., “LLaMoCo: Instruction Tuning of Large Language Models for Optimization Code Generation,” IEEE Transactions on Evolutionary Computation (TEC), 2025.
5. Z. Ma, H. Guo, Y.-J. Gong*, et al., “Toward Automated Algorithm Design: A Survey and Practical Guide to Meta-Black-Box-Optimization,” IEEE Transactions on Evolutionary Computation (TEC), 2025.
6. Z. Ma, J. Chen, H. Guo, Y.-J. Gong*, “Neural Exploratory Landscape Analysis for Meta-Black-Box-Optimization,” The Thirteenth International Conference on Learning Representations (ICLR), 2025.
7. Z. Ma, Z. Cao, Z. Jiang, H. Guo, Y.-J. Gong*, “Meta-Black-Box-Optimization through Offline Q-function Learning,” Forty-Second International Conference on Machine Learning (ICML), 2025.
8. H. Guo, Z. Ma, Y. Ma, Z. Cao, and Y.-J. Gong*, “ConfigX: Modular Configuration for Evolutionary Algorithms via Multitask Reinforcement Learning,” The 39th AAAI Conference on Artificial Intelligence (AAAI), 2025.
9. Y. Zhong and Y.-J. Gong*, “Data-Driven Evolutionary Computation under Continuously Streaming Environments: A Drift-Aware Approach,” IEEE Transactions on Evolutionary Computation (TEC), 2025.
10. Q. Li, Z. Cao, Y. Ma, Y. Wu, and Y.-J. Gong*, “Diversity Optimization for Travelling Salesman Problem via Deep Reinforcement Learning,” ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD), 2025.
11. H. Guo, Y. Ma, Z. Ma, J. Chen, X. Zhang, Z. Cao, J. Zhang, and Y.-J. Gong*, “Deep Reinforcement Learning for Dynamic Algorithm Selection: A Proof-of-Principle Study on Differential Evolution,” IEEE Transactions on Systems, Man and Cybernetics: Systems (TSMC-Systems), 2024.
12. J. Chen, Z. Ma, H. Guo, Y. Ma, J. Zhang, and Y.-J. Gong*, “SYMBOL: Generating Flexible Black-Box Optimizers through Symbolic Equation Learning,” International Conference on Learning Representations (ICLR), 2024.
13. Y.-J. Gong, Y.-T. Zhong, and H.-G. Huang, “Offline Data-Driven Optimization at Scale: A Cooperative Coevolutionary Approach,” IEEE Transactions on Evolutionary Computation (TEC), 2024.
14. Z. Ma, H. Guo, J. Chen, Z. Li, G. Peng, Y.-J. Gong*, Y. Ma, and Z. Cao, “MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning,” Advances in Neural Information Processing Systems (NeurIPS), 2023.
15. H.-G. Huang and Y.-J. Gong*, “Contrastive Learning: An Alternative Surrogate for Offline Data-Driven Evolutionary Computation,” IEEE Transactions on Evolutionary Computation (TEC), vol. 27, no. 2, pp. 370-384, 2023.
Awards：
1. Stanford University's Top 2% of Global Scientists, 2023, 2024, 2025
2. Guangzhou Leading Talent Scholar in Science and Technology, 2024
3. Guangdong Distinguished Young Scholar, 2022
4. TCL Young Scholar, 2022
5. SCUT Xinghua Scholar, 2022, 2017
6. DiDi Gaia Young Scholar, 2020
7. Pearl River Young Scholar, 2017
8. ACM Guangzhou Excellent Doctoral Dissertation Award, 2015
9. HPC Collaborative Innovation Center Best Doctoral Dissertation Award, 2015
10. Google Anita Borg Scholar, 2013
