Professor
School of Computer Science and Technology,
South China University of Technology
Biography
I am Dr. Yue-Jiao Gong, a Full Professor in the School of Computer Science and Engineering, South China University of Technology (SCUT), China. I received my B.S. and Ph.D. degrees in Computer Science from Sun Yat-sen University (SYSU) in 2010 and 2014, respectively, under the supervision of Professor Jun Zhang. I also had the privilege of expanding my horizons as a postdoctoral research fellow in the Department of Computer and Information Science at the University of Macau (UM) from 2015 to 2016, with Professor Yicong Zhou, and as a research assistant in the Hong Kong University of Science and Technology (HKUST) in 2014, alongside Professor Lionel M. Ni.
My research interests span the fields of Computational Intelligence, Evolutionary Optimization, and Machine Learning, with a particular focus on applying these techniques in Intelligent Transportation Systems, as well as addressing several fundamental tasks in Data Mining and Image Processing.
Awards & Honors
Throughout my academic journey, I have been honored to receive several awards & honors, including:
- World's Top 2% Scientists on the Stanford University Released List (2023,2024)
- Guangzhou Leading Talent Scholar in Science and Technology (2024)
- SCUT-TCL Young Scholar (2022)
- Guangdong Distinguished Young Scholar (2022)
- DiDi Gaiya Young Scholar (2020)
- IEEE Senior Member (2019)
- Pearl River Young Scholar (2017)
- SCUT Xinghua Scholar (2017)
- ACM Guangzhou Excellent Doctoral Dissertation Award (2015)
- HPC Collaborative Innovation Center Best Doctoral Dissertation Award (2015)
- Outstanding Reviewer for IEEE Trans. Cybern. (2015)
- Google Anita Borg Scholar (2013)
- The 1st Prize in the Competition of the 4th National Information Science Doctoral Forum (2013)
- Top 1st GPA among 178 students in the Department of Computer Science, SYSU, during the four-year undergraduate program (2006-2010)
Paper and Code Hub
We provide two GitHub repositories that maintain lists of relevant papers and open-source code on the following topics:
(any suggestions for updates are welcome)
Selected Publications
Below, you'll find a categorized selection of featured articles, sorted by areas of expertise. An asterisk* indicates that I'm (one of) the corresponding author(s). For a comprehensive list of my publications, please visit my profiles on Google Scholar and DBLP.
1. Learning for Optimization: I began my research in this field in 2020, exploring a range of fascinating topics such as Meta-Black-Box Optimization (MetaBBO), Data-Driven Evolutionary Optimization (DDEO), and Neural Combinatorial Optimization (NCO).
[MetaBBO] Z. Ma, H. Guo, Y.-J. Gong*, J. Zhang, K. C. Tan, "Toward Automated Algorithm Design: A Survey and Practical Guide to Meta-Black-Box-Optimization," IEEE Transactions on Evolutionary Computation (TEC), 2025.
[MetaBBO] Z. Ma, H. Guo, J. Chen, G. Peng, Z. Cao, Y. Ma, Y.-J. Gong*, "LLaMoCo: Instruction Tuning of Large Language Models for Optimization Code Generation," https://arxiv.org/abs/2403.01131
[MetaBBO] 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. [Code]
[MetaBBO] 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. [Code]
[MetaBBO] 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. [Code]
[MetaBBO] 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. [Code]
[MetaBBO] 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. [Code]
[MetaBBO] 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. [Code] & [Webpage]
[MetaBBO] Z. Ma, J. Chen, H. Guo, Y. Ma, Y.-J. Gong*, “Auto-configuring Exploration-Exploitation Tradeoff in Evolutionary Computation via Deep Reinforcement Learning,” Genetic and Evolutionary Computation Conference (GECCO), 2024. [Code]
[DDEO] Y. Zhong, X. Wang, Y. Sun, Y.-J. Gong*, “SDDObench: A Benchmark for Streaming Data-Driven Optimization with Concept Drift,” Genetic and Evolutionary Computation Conference (GECCO), 2024. [Code]
[DDEO] 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. [Code]
[DDEO] X.-R. Zhang, Y.-J. Gong*, Z. Cao, J. Zhang, “Island-Based Evolutionary Computation with Diverse Surrogates and Adaptive Knowledge Transfer for High-Dimensional Data-Driven Optimization,” ACM Transactions on Evolutionary Learning and Optimization (TELO), 2024. [Code]
[DDEO] 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. [Code]
[DDEO] Y.-J. Gong, J.-X. Guo, D.-L. Lin, et al., “Automated Team Assembly in Mobile Games: A Data-Driven Evolutionary Approach using a Deep Learning Surrogate,” IEEE Transactions on Games (TG), vol. 15, no. 1, pp. 67-80, 2023.
[NCO] 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. [Code]
[NCO] J. Chen, J. Wang, Z. Cao, Y. Wu, H. Qin, Z. Zhang and Y.-J. Gong, “Rethinking Neural Multi-Objective Combinatorial Optimization via Neat Weight Embedding,” The Thirteenth International Conference on Learning Representations (ICLR), 2025. [Code]
[NCO] Y. Ma, J. Li, Z. Cao, W. Song, H. Guo, Y.-J. Gong, et al., “Efficient Neural Neighborhood Search for Pickup and Delivery Problems,” The 31st International Joint Conference on Artificial Intelligence (IJCAI), 2022. [Code]
2. Evolutionary Optimization: I possess over 15 years of research experience in the field of Evolutionary Optimization, with a particular emphasis on Diversity Optimization (DO), Cooperative Coevolution (CC), and Tensorial Computation (TC).
[TC] S.-C. Lei, Y.-J. Gong*, X. Xiao, et al., "Tensorial Evolutionary Optimization for Natural Image Matting," ACM Transactions on Multimedia Computing, Communications and Applications (ACM TOMM), 2024. [Code]
[TC] S.-C. Lei, X. Xiao, Y.-J. Gong*, et al., "Tensorial Evolutionary Computation for Spatial Optimization Problems," IEEE Transactions on Artificial Intelligence (TAI), 2023. [Code]
[DO] T. Huang, Y.-J. Gong*, W.-N. Chen, et al., “A Probabilistic Niching Evolutionary Computation Framework Based on Binary Space Partitioning”, IEEE Transactions on Cybernetics (TC), vol. 52, no. 1, pp. 51-64, 2022. [Code]
[DO] T. Huang, Y.-J. Gong*, S. Kwong, et al., “A Niching Memetic Algorithm for Multi-Solution Traveling Salesman Problem,” IEEE Transactions on Evolutionary Computation (TEC), vol. 24, no. 3, pp. 508-522, 2020. [Webpage with Code]
[DO] Y.-H. Zhang, Y.-J. Gong*, Y. Gao, et al., “Parameter-Free Voronoi Neighborhood for Evolutionary Multimodal Optimization,” IEEE Transactions on Evolutionary Computation (TEC), vol. 24, no. 2, pp. 335-349, 2020. [Code]
[CC] A. Song, W.-N. Chen, Y.-J. Gong, et al., “A Divide-and-conquer Evolutionary Algorithm for Large-scale Virtual Network Embedding,” IEEE Transactions on Evolutionary Computation (TEC), vol. 24, no. 3, pp.566-580, 2020.
[CC] X.-Y. Zhang, Y.-J. Gong*, Y. Lin, et al., “Dynamic Cooperative Coevolution for Large Scale Optimization,” IEEE Transactions on Evolutionary Computation (TEC), vol. 23, no. 6, pp.935-948, 2019. [Code]
[CC] Y.-H. Zhang, Y.-J. Gong*, H.-X. Zhang, et al, “DECAL: A Decomposition-Based Coevolutionary Algorithm for Many-Objective Optimization”, IEEE Transactions on Cybernetics (TC), vol. 49, no. 1, pp. 27-41, 2019.
[DO] Y.-H. Zhang, Y.-J. Gong*, H.-X. Zhang, et al, “Toward Fast Niching Evolutionary Algorithms: A Locality Sensitive Hashing-Based Approach,” IEEE Transactions on Evolutionary Computation (TEC), vol. 21, no. 3, pp. 347-362, 2017. [Code]
[DO] Y.-J. Gong, J. Zhang, and Y. Zhou, “Learning Multimodal Parameters: A Bare-Bones Niching Differential Evolution Approach,” IEEE Transactions on Neural Network and Learning Systems (TNNLS), vol. 29, no. 7, pp. 2944-2959, 2018.
[CC] Y.-J. Gong, J.-J. Li, Y. Zhou, et al., "Genetic Learning Particle Swarm Optimization," IEEE Transactions on Cybernetics (TC), vol. 46, no. 10, pp. 2277-2290, 2016. [Code]
3. Optimization & Learning in Intelligent Transportation Systems: This is my primary application area of expertise.
- Y.-J. Gong, T. Huang, Y.-N. Ma, et al., “MTrajPlanner: A Multiple-Trajectory Planning Algorithm for Autonomous Underwater Vehicles,” IEEE Transactions on Intelligent Transportation Systems (TITS), vol. 24, no. 4, pp. 3714-3727, 2023. [Code]
- W. Zhou, X. Xiao, Y.-J. Gong*, et al., " Travel Time Distribution Estimation by Learning Representations over Temporal Attributed Graphs," IEEE Transactions on Intelligent Transportation Systems (TITS), vol. 24, no. 5, pp. 5069-5081, 2023.
- Z. Chen, X. Xiao, Y.-J. Gong* , et al., “Interpreting Trajectories from Multiple Views: A Hierarchical Self-Attention Network for Estimating the Time of Arrival,” 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Washington DC, USA, 2022. [Code]
- X.-X. Shao, Y.-J. Gong*, Z.-H. Zhan, et al., “Bipartite Cooperative Coevolution for Energy-Aware Coverage Path Planning of UAVs,” IEEE Transactions on Artificial Intelligence (TAI), vol. 3, no. 1, pp. 29-42, 2022.
- Y.-J. Gong, Y.-W. Liu, Y. Lin, et al., “Real-Time Taxi-Passenger Matching Using a Differential Evolutionary Fuzzy Controller,”, IEEE Transactions on Systems, Man and Cybernetics: Systems (TSMC-Systems), vol. 51, no. 5, pp. 2712-2725, 2021.
- T. Huang, Y.-J. Gong*, Y.-H. Zhang, et al., “Automatic Planning of Multiple Itineraries: A Niching Genetic Evolution Approach,” IEEE Transactions on Intelligent Transportation Systems (TITS), vol. 21, no. 10, pp. 4225-4240, 2020.
- W.-L. Liu, Y.-J. Gong*, W.-N. Chen, et al., “Coordinated Charging Scheduling of Electric Vehicles: A Mixed-Variable Differential Evolution Approach,” IEEE Transactions on Intelligent Transportation Systems (TITS), vol. 21, no. 12, pp. 5094-5109, 2020.
- Y.-N. Ma, Y.-J. Gong* , C.-F. Xiao, et al., “Path Planning for Autonomous Underwater Vehicles: An Ant Colony Algorithm Incorporating Alarm Pheromone,” IEEE Transactions on Vehicular Technology (TVT), vol. 68, no. 1, pp. 141-154, 2019.
- Y.-H. Zhang, Y.-J. Gong*, W.-N. Chen, et al, “A Dual-Colony Ant Algorithm for the Receiving and Shipping Door Assignments in Cross-Docks”, IEEE Transactions on Intelligent Transportation Systems (TITS), vol. 20, no. 7, pp. 2523-2539, 2019.
- Y.-J. Gong, E. Chen, Lionel M. Ni, et al, “AntMapper: An Ant Colony-Based Map Matching Approach for Trajectory-Based Applications,” IEEE Transactions on Intelligent Transportation Systems (TITS), vol. 19, no. 2, pp. 390-401, 2018.
4. Optimization & Learning in Fundamental Tasks of Data Mining and Image Processing: My postdoctoral experience equipped me with a solid foundation in data mining and image processing, and I continue to apply the latest optimization and learning techniques to address fundamental tasks within the areas.
- X. Xiao, Y. Wu, and Y.-J. Gong*, “Relative Comparison-based Consensus Learning for Multi-view Subspace Clustering,”IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2024.
- X. Xiao and Y.-J. Gong*, “Accurate Complementarity Learning for Graph-based Multi-view Clustering,” IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2023.
- J.-X. Chen, Y.-J. Gong*, W.-N. Chen, et al., “EvoS&R: Evolving Multiple Seeds and Radii For Varying Density Data Clustering,” IEEE Transactions on Knowledge and Data Engineering (TKDE), 2023. [Code]
- S.-C. Lei, Y.-J. Gong*, X. Xiao, et al., "Boosting Diversity in Visual Search with Pareto Non-Dominated Re-Ranking," ACM Transactions on Multimedia Computing Communications and Applications (ACM TOMM), 2023. [Code]
- J.-X. Chen, Y.-J. Gong*, W.-N. Chen, et al., “Elastic Differential Evolution for Automatic Data Clustering”, IEEE Transactions on Cybernetics (TC), vol. 51, no. 8, pp. 4134-4147, 2021. [Code]
- X. Xiao, Y. Chen, Y.-J. Gong*, et al., “Low-Rank Preserving t-Linear Projection for Robust Image Feature Extraction,” IEEE Transactions on Image Processing (TIP), vol. 30, pp. 108-120, 2021.
- X. Xiao, Y.-J. Gong*, Z. Hua, et al., “On Reliable Multi-View Affinity Learning for Subspace Clustering,” IEEE Transactions on Multimedia (TMM), vol. 23, pp. 4555-4566, 2021.
- X. Xiao, Y. Chen, Y.-J. Gong, et al., “"Prior Knowledge Regularized Multi-view Self-Representation and Its Applications,” IEEE Transactions on Neural Networks and Learning Systems (TNNLS), vol. 32, no. 3, pp. 1352-1338, 2021.
- X.-L. Xiao, Y. Zhou, and Y.-J. Gong*, “RGB-‘D’ Saliency Detection With Pseudo Depth,” IEEE Transactions on Image Processing (TIP), vol. 28, no. 5, pp. 2126-2139, 2019.
- X.-L. Xiao, Y. Zhou, and Y.-J. Gong* ,“Content Adaptive Superpixel Segmentation,” IEEE Transactions on Image Processing (TIP), vol. 27, no. 6, pp. 2883 – 2896, 2018.
- Y.-J. Gong and Y. Zhou, “Differential Evolutionary Superpixel Segmentation,” IEEE Transactions on Image Processing (TIP), vol. 27, no. 3, pp. 1390-1404, 2018.
Scholat.com/yjgong
Guangzhou Higher Edcucation Megacenter
