Publications

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, Y.-J. Gong*, H. Guo, W. Qiu, S. Ma, et al., “MetaBox-v2: A Unified Benchmark Platform for Meta-Black-Box Optimization,” https://arxiv.org/abs/2505.17745

[MetaBBO] H. Guo, Z. Ma, Y. Ma, X. Zhang, W.-N. Chen, Y.-J. Gong*, “DesignX: Human-Competitive Algorithm Designer for Black-Box Optimization,” https://arxiv.org/abs/2505.17866

[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, 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, 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]

[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]

[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] 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.

[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.

J. Li, Z. Ma, T. Huang, Y.-J. Gong*, “Learn to Refine: Synergistic Multi-Agent Path Optimization for Lifelong Conflict-Free Navigation of Autonomous Vehicles,” ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD), 2025.

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.