Shengcai Liu（刘晟材）

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副研究员（助理教授），博导
Tenure-track Assistant Professor
Department of Computer Science and Engineering
Southern University of Science and Technology

Office: Room 311, South Tower, College of Engineering
Address: No. 1088 Xueyuan Avenue, Shenzhen, Guangdong, China 518055
Email: liusc3 [AT] sustech [DOT] edu [DOT] cn, liusccc [AT] gmail [DOT] com

I received my B.Sc. and Ph.D. degrees in the School of Computer Science and Technology from USTC in 2014 and 2020, respectively, advised by Prof. Xin Yao and Prof. Ke Tang. From Jan 2021 to Jan 2023, I was a Research Assistant Professor at the CSE Department of SUSTech. From Jan 2023 to May 2024, I was a Visiting Professor, and later a senior scientist, at CFAR, A*STAR, Singapore, in collaboration with Prof. Yew-Soon Ong from NTU. I am also a member of the Nature Inspired Computation and Applications Laboratory (NICAL) led by Prof. Xin Yao and Prof. Ke Tang.

Research Interest

Ultimately, I am obsessed with the theoretical foundations and practical approaches for the fully-automated design of optimization algorithms and learning models.

Now I am interested in:

  • Theoretical foundations of Learning to Optimize (L2O)

  • Learning scalable optimizers

  • Learning general-purpose optimizers

  • Evolutionary Large Learning Models

I am looking for self-motivated Master students, Ph.D. students, and Postdocs, working in the above research directions. If you are interested, please feel free to contact me via emails.

Selected Publications

First/corresponding-authored (*) works listed; see Google Scholar for complete list.

Preprints

  1. Zubin Zheng, Shengcai Liu*, and Yew-Soon Ong. Hybrid Memetic Search for Electric Vehicle Routing with Time Windows, Simultaneous Pickup-Delivery, and Partial Recharges. Arxiv preprint arXiv:2410.19580. [Arxiv]

  2. Shengcai Liu, Zhiyuan Wang, Yew-Soon Ong, Xin Yao, and Ke Tang. Learning Mixture-of-Experts for General-Purpose Black-Box Discrete Optimization. Arxiv preprint arXiv:2405.18884. [Arxiv]

  3. Qingya Li, Shengcai Liu*, Juan Zou, and Ke Tang. A Novel Dual-Stage Algorithm for Capacitated Arc Routing Problems with Time-Dependent Service Costs. ArXiv preprint arXiv:2406.15416. [Arxiv]

  4. Wenjie Chen, Shengcai Liu*, Yew-Soon Ong, and Ke Tang. Neural Influence Estimator: Towards Real-time Solutions to Influence Blocking Maximization. Arxiv preprint arXiv:2308.14012. [Arxiv]

Journal Papers

  1. Jiahao Wu, Wenqi Fan, Jingfan Chen, Shengcai Liu*, Qijiong Liu, Rui He, Qing Li, and Ke Tang. Condensing Pre-augmented Recommendation Data via Lightweight Policy Gradient Estimation. IEEE Transactions on Knowledge and Data Engineering, 2024, DOI: 10.1109/TKDE.2024.3484249. [Arxiv]

  2. Xuanfeng Li, Shengcai Liu*, and Ke Tang. Novel Genetic Algorithm for Solving Chance-Constrained Multiple-Choice Knapsack Problems. Journal of Computer Applications, 2024, 44(5): 1378-1385. [Paper]

  3. Xuanfeng Li, Shengcai Liu*, Jin Wang, Xiao Chen, Yew-Soon Ong, and Ke Tang. Chance-Constrained Multiple-Choice Knapsack Problem: Model, Algorithms, and Applications. IEEE Transactions on Cybernetics, 2024, DOI: 10.1109/TCYB.2024.3402395. [Paper] [Arxiv][Code]

  4. Ning Lu, Shengcai Liu*, Rui He, Qi Wang, Yew-Soon Ong, and Ke Tang. Large Language Models can be Guided to Evade AI-Generated Text Detection. Transactions on Machine Learning Research, 2024. [Paper] [Arxiv][Code]

  5. Shengcai Liu, Ning Lu, Wenjing Hong, Chao Qian, and Ke Tang. Effective and Imperceptible Adversarial Textual Attack via Multi-objectivization. ACM Transactions on Evolutionary Learning and Optimization, 2024, 4(3): 16:1-16:23. [Paper] [Arxiv][Code]

  6. Shengcai Liu, Yu Zhang, Ke Tang, and Xin Yao. How Good Is Neural Combinatorial Optimization? A Systematic Evaluation on the Traveling Salesman Problem. IEEE Computational Intelligence Magazine, 2023, 18(3): 14-28. [Paper][Arxiv][Code]

  7. Zeyu Dai, Shengcai Liu*, Qing Li, and Ke Tang. Saliency Attack: Towards Imperceptible Black-box Adversarial Attack. ACM Transactions on Intelligent Systems and Technology, 2023, 14(3): 1-20. [Paper][Arxiv][Code]

  8. Rui He, Shengcai Liu*, Shan He, and Ke Tang. Multi-Domain Active Learning: Literature Review and Comparative Study. IEEE Transactions on Emerging Topics in Computational Intelligence, 2023, 7(3): 791-804. [Paper][Arxiv]

  9. Shengcai Liu, Ning Lu, Cheng Chen, and Ke Tang. Efficient Combinatorial Optimization for Word-level Adversarial Textual Attack. IEEE/ACM Transactions on Audio, Speech and Language Processing, 2022, 30: 98-111. [Paper][Arxiv][Code]

  10. Shengcai Liu, Peng Yang, and Ke Tang. Approximately Optimal Construction of Parallel Algorithm Portfolios by Evolutionary Intelligence (in Chinese). SCIENTIA SINICA Technologica, 2023, 53(2): 280-290. [Paper]

  11. Shengcai Liu, Ke Tang, and Xin Yao. Memetic Search for Vehicle Routing with Simultaneous Pickup-Delivery and Time Windows. Swarm and Evolutionary Computation, 66: 100927, 2021. [Paper][Arxiv][Code]

  12. Shengcai Liu, Ke Tang, Peng Yang, and Xin Yao. Few-shots Parallel Algorithm Portfolio Construction via Co-evolution. IEEE Transactions on Evolutionary Computation, 2021, 25(3): 595-607. [Paper] [Arxiv][Code]

  13. Shengcai Liu, Ke Tang, and Xin Yao. Generative Adversarial Construction of Parallel Portfolios. IEEE Transactions on Cybernetics, 2022, 52(2): 784-795. [Paper]

Conference Papers

  1. Ning Lu, Shengcai Liu*, Zhirui Zhang, Qi Wang, Haifeng Liu, and Ke Tang. Less is More: Understanding Word-level Textual Adversarial Attack via n-gram Frequency Descend. In: Proceedings of The 2024 IEEE Conference on Artificial Intelligence (CAI’2024), Singapore, Singapore, 2024, 823-830. [Paper] [Arxiv]

  2. Shengcai Liu, Caishun Chen, Xinghua Qu, Ke Tang, and Yew-Soon Ong. Large Language Models as Evolutionary Optimizers. In: Proceedings of The 2024 IEEE Congress on Evolutionary Computation (CEC’2024), Yokohama, Japan, 2024, 1-8. [Paper] [Arxiv]

  3. Rui He, Shengcai Liu*, Jiahao Wu, Shan He, and Ke Tang. Multi-Domain Learning From Insufficient Annotations. In: Proceedings of The 26th European Conference on Artificial Intelligence (ECAI’2023), Kraków, Poland, 2023, 1028-1035. [Paper][Arxiv]

  4. Shengcai Liu, Fu Peng, and Ke Tang. Reliable Robustness Evaluation via Automatically Constructed Attack Ensembles. In: Proceedings of The 37th AAAI Conference on Artificial Intelligence (AAAI’2023), Washington, DC, 2023, 8852-8860. [Paper][Arxiv][Code]

  5. Fu Peng, Shengcai Liu*, and Ke Tang. Training Quantized Deep Neural Networks via Cooperative Coevolution. In: Proceedings of the 13th International Conference on Swarm Intelligence (ICSI’2022), Xi'an, China, 2022, 81-93. [Paper]

  6. Kangfei Zhao, Shengcai Liu*, Yu Rong, and Jeffrey Xu Yu. Towards Feature-free TSP Solver Selection: A Deep Learning Approach. In: Proceedings of the 20th International Joint Conference on Neural Networks (IJCNN’2021), Virtual Event, 2021, 1-8. [Paper][Arxiv][Code]

  7. Shengcai Liu, Ke Tang, and Xin Yao. On Performance Estimation in Automatic Algorithm Configuration. In: Proceedings of The 34th AAAI Conference on Artificial Intelligence (AAAI’2020), New York, NY, 2020, 2384-2391. [Paper]

  8. Shengcai Liu, Ke Tang, and Xin Yao. Automatic Construction of Parallel Portfolios via Explicit Instance Grouping. In: Proceedings of The 33rd AAAI Conference on Artificial Intelligence (AAAI’2019), Honululu, HI, 2019, 1560-1567. [Paper]

  9. Shengcai Liu, Yufan Wei, Ke Tang, A.K. Qin, and Xin Yao. Qos-aware Long-Term Based Service Composition in Cloud Computing. In: Proceedings of The 14th IEEE Congress on Evolutionary Computation (CEC’2015), Sendai, Japan, 2015, 3362-3369. [Paper]

Grants

  • 项目负责人, “高可靠组合优化求解器的自动构建关键方法”, 华为-南方科技大学计算机系人工 智能RAMS技术创新实验室长期合作框架协议项目, 05/2021 - 05/2022, CNY 400,000.

Invited Talks

  • 通用优化探索之路: 从并行算法组自动构造到优化基座模型 @ NICE Seminar. Aug 11, 2024.

  • Learn to Optimize @ The 2022 IEEE World Congress on Computational Intelligence (WCCI’2022). Jul 18, 2022. [Slides]

  • Co-Evolved Parallel Algorithm Portfolios @ The 7th Workshop on Evolutionary Computation and Learning (ECOLE’2021). May 15, 2021.

  • Algorithm Portfolios for Beginners @ Magic-Data (数据魔术师). Apr 6, 2021. (online)

Teaching

  • CS110-Java程序设计基础 (for undergraduate students, 2024 Fall)