Shengcai Liu(刘晟材)

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Senior Scientist, Centre for Frontier AI Research (CFAR)
Agency for Science, Technology and Research (A*STAR)
Address: 1 Fusionopolis Way, #16-16, Connexis North Tower, Singapore 138632
Email: liusccc [AT] gmail [DOT] com

Short Biography

I received my B.Sc. and Ph.D. degrees in the School of Computer Science and Technology from the University of Science and Technology of China (USTC) in 2014 and 2020, respectively, under the supervision of Prof. Xin Yao and Prof. Ke Tang.

From Jan 2021 to Jan 2023, I was a Research Assistant Professor at the Department of Computer Science and Engineering (CSE), Southern University of Science and Technology (SUSTech). I am also a member of the Nature Inspired Computation and Applications Laboratory (NICAL) led by Prof. Xin Yao and Prof. Ke Tang.

From Jan 2023 to May 2023, I was a Visting Professor at the Institute of High Performance Computing (IHPC), A*STAR, Singapore. Currently I am a research scientist at CFAR. I am in collaboration with Prof. Yew-Soon Ong from Nanyang Technological University (NTU).

Research Interest

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

Now I am working on:

  • Algorithm design by Evolutionary Computation, i.e., Algorithm Evolution

  • Deep learning to algorithm design, i.e., Learn to Optimize

  • Applications of these methods to practical combinatorial optimization problems such as evaluation of the robustness/safety of AI models (such as large language models), complex vehicle routing, influence blocking maximization, and 5G network end-to-end configuration

Recent News

  • Feb 29, 2024: Our work “Effective and Imperceptible Adversarial Textual Attack via Multi-objectivization” has been accepted by ACM TELO

  • Nov 23, 2023: Our work “Datastore Distillation for Nearest Neighbor Machine Translation” has been accepted by IEEE TASL

  • Aug 18, 2023: I experienced a wonderful week attending the Dagstuhl Seminar held in a secluded village in Germany.

  • Aug 6, 2023: Our work “A Population Cooperation Based Particle Swarm Optimization Algorithm for Large-Scale Multi-Objective Optimization” has been accepted by SWEVO

  • Jul 16, 2023: Our work “Multi-Domain Learning From Insufficient Annotations” has been accepted by ECAI’2023

  • Apr 9, 2023: Our work “How Good Is Neural Combinatorial Optimization? A Systematic Evaluation on the Traveling Salesman Problem” has been accepted by IEEE CIM

Selected Publications

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


  1. Shengcai Liu, Caishun Chen, Xinghua Qu, Ke Tang, and Yew-Soon Ong. Large Language Models as Evolutionary Optimizers. Arxiv preprint arXiv:2310.19046. [Arxiv]

  2. 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]

  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. Arxiv preprint arXiv:2306.14690. [Arxiv][Code]

  4. Ning Lu, Shengcai Liu*, Rui He, Qi Wang, and Ke Tang. Large Language Models can be Guided to Evade AI-Generated Text Detection. Arxiv preprint arXiv:2305.10847. [Arxiv][Code]

  5. 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. Arxiv preprint arXiv:2302.02568. [Arxiv]

Journal Papers

  1. 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, To Appear. [Arxiv][Code]

  2. 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]

  3. 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]

  4. 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]

  5. 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]

  6. 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]

  7. 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]

  8. 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]

  9. 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. 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, To appear. [Arxiv]

  2. 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]

  3. 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]

  4. 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]

  5. 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]

  6. 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]

  7. 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]

Invited Talks

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

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

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

Code & Datasets

VRPenstein: A highly-flexible and high-performance meta-heuristic with considerable large configuration space for the vehicle routing problems (VRP).

GA-EAX-restart: A restart version of the powerful Genetic Algorithm with Edge Assembly Crossover (GA-EAX by Nagata & Kobayashi). In our massive experiments, GA-EAX-restart could consistently outperform GA-EAX and LKH 2.0.9, the two state-of-the-art inexact solvers for TSP.

Community Services

One of the editors of All About Evolutionary Optimization (AAEO) (i.e., ECOLE weekly).

Journal reviewers: IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Evolutionary Computation, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Cybernetics, IEEE Transactions on Emerging Topics in Computational Intelligence, IEEE Computational Intelligence Magazine, ACM Transactions on Asian and Low-Resource Language Information Processing, Journal of Artificial Intelligence, Frontiers of Computer Science, Information Sciences, Memetic Computing, Natural Computing, Swarm and Evolutionary Optimization.

PC members: AAAI 2019/2020/2021/2022/2023, IJCAI 2020/2021/2022/2023, ICML 2021, NeurIPS 2021

Member: IEEE, AAAI