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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.1//EN"
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<title>Shengcai Liu（刘晟材）</title>
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<h1>Shengcai Liu（刘晟材）</h1>
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<img src="photos/bio_1.jpg" alt="alt text" width="165px" height="225px" /> </td>
<td align="left"><p>副研究员（助理教授），博导<br />
Tenure-track Assistant Professor<br />
Department of Computer Science and Engineering<br />
Southern University of Science and Technology<br /></p>
<p>Office: Room 311, South Tower, College of Engineering<br />
Address: No. 1088 Xueyuan Avenue, Shenzhen, Guangdong, China 518055<br />
Email: liusc3 [AT] sustech [DOT] edu [DOT] cn, liusccc [AT] gmail [DOT] com</p>
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<p>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 <a href="https://www.cs.bham.ac.uk/~xin/">Prof. Xin Yao</a> and <a href="http://faculty.sustech.edu.cn/tangk3/en/">Prof. Ke Tang</a>.
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 <a href="https://personal.ntu.edu.sg/asysong/home.html">Prof. Yew-Soon Ong</a> from NTU.
I am also a member of the Nature Inspired Computation and Applications Laboratory (<a href="http://www.nical.ai/">NICAL</a>) led by Prof. Xin Yao and Prof. Ke Tang.</p>
<h2>Research Interest</h2>
<p>Ultimately, I am obsessed with the theoretical foundations and practical approaches for <b>the fully-automated design of optimization algorithms and learning models</b>.</p>
<p>Now I am interested in:</p>
<ul>
<li><p>Theoretical foundations of Learning to Optimize (L2O)</p>
</li>
<li><p>Learning scalable optimizers</p>
</li>
<li><p>Learning general-purpose optimizers</p>
</li>
<li><p>Evolutionary Large Learning Models
</p>
</li>
</ul>
<p><span style="color: red;">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.</span></p>
<h2>Selected Publications</h2>
<p>First/corresponding-authored (*) works listed; see <a href="https://scholar.google.com/citations?user=tV0nV3oAAAAJ&hl=en">Google Scholar</a> for complete list.</p>
<h3>Preprints</h3>
<ol>
<li><p>Zhiyuan Wang, Shengcai Liu, Peng Yang, and Ke Tang. Domain-Agnostic Co-Evolution of Generalizable Parallel Algorithm Portfolios. <i>Arxiv preprint arXiv:2501.02906</i>. [<a href="https://arxiv.org/pdf//2501.02906.pdf">Arxiv</a>]</p>
</li>
<li><p>Zubin Zheng, Shengcai Liu, and Yew-Soon Ong. Hybrid Memetic Search for Electric Vehicle Routing with Time Windows, Simultaneous Pickup-Delivery, and Partial Recharges. <i>Arxiv preprint arXiv:2410.19580</i>. [<a href="https://arxiv.org/pdf//2410.19580.pdf">Arxiv</a>]</p>
</li>
<li><p>Shengcai Liu, Zhiyuan Wang, Yew-Soon Ong, Xin Yao, and Ke Tang. Learning Mixture-of-Experts for General-Purpose Black-Box Discrete Optimization. <i>Arxiv preprint arXiv:2405.18884</i>. [<a href="https://arxiv.org/pdf//2405.18884.pdf">Arxiv</a>]</p>
</li>
<li><p>Wenjie Chen, Shengcai Liu, Yew-Soon Ong, and Ke Tang. Neural Influence Estimator: Towards Real-time Solutions to Influence Blocking Maximization. <i>Arxiv preprint arXiv:2308.14012</i>. [<a href="https://arxiv.org/pdf//2308.14012.pdf">Arxiv</a>]</p>
</li>
</ol>
<h3>Journal Papers</h3>
<ol>
<li><p>Jiahao Wu, Wenqi Fan, Jingfan Chen, <b>Shengcai Liu</b>*, Qijiong Liu, Rui He, Qing Li, and Ke Tang. Condensing Pre-augmented Recommendation Data via Lightweight Policy Gradient Estimation. <i>IEEE Transactions on Knowledge and Data Engineering</i>, 2025, 37(1): 162 - 173. [<a href="https://ieeexplore.ieee.org/abstract/document/10726790">Paper</a>] [<a href="https://arxiv.org/pdf//2310.01038.pdf">Arxiv</a>]</p>
</li>
<li><p>Xuanfeng Li, <b>Shengcai Liu</b>*, and Ke Tang. Novel Genetic Algorithm for Solving Chance-Constrained Multiple-Choice Knapsack Problems. <i>Journal of Computer Applications</i>, 2024, 44(5): 1378-1385. [<a href="http://www.joca.cn/EN/Y2024/V44/I5/1378">Paper</a>]</p>
</li>
<li><p>Xuanfeng Li, <b>Shengcai Liu</b>*, Jin Wang, Xiao Chen, Yew-Soon Ong, and Ke Tang. Chance-Constrained Multiple-Choice Knapsack Problem: Model, Algorithms, and Applications. <i>IEEE Transactions on Cybernetics</i>, 2024, 54(12): 7969 - 7980. [<a href="https://ieeexplore.ieee.org/abstract/document/10549845">Paper</a>] [<a href="https://arxiv.org/pdf//2306.14690.pdf">Arxiv</a>][<a href="https://github.com/eddylxf23/DDCCMCKP">Code</a>]</p>
</li>
<li><p>Ning Lu, <b>Shengcai Liu</b>*, Rui He, Qi Wang, Yew-Soon Ong, and Ke Tang. Large Language Models can be Guided to Evade AI-Generated Text Detection. <i>Transactions on Machine Learning Research</i>, 2024. [<a href="https://openreview.net/forum?id=lLE0mWzUrr">Paper</a>] [<a href="https://arxiv.org/pdf/2305.10847.pdf">Arxiv</a>][<a href="https://github.com/ColinLu50/Evade-GPT-Detector">Code</a>]</p>
</li>
<li><p><b>Shengcai Liu</b>, Ning Lu, Wenjing Hong, Chao Qian, and Ke Tang. Effective and Imperceptible Adversarial Textual Attack via Multi-objectivization. <i>ACM Transactions on Evolutionary Learning and Optimization</i>, 2024, 4(3): 16:1-16:23. [<a href="https://dl.acm.org/doi/full/10.1145/3651166">Paper</a>] [<a href="https://arxiv.org/pdf/2111.01528.pdf">Arxiv</a>][<a href="https://github.com/ColinLu50/HydraText">Code</a>]</p>
</li>
<li><p><b>Shengcai Liu</b>, Yu Zhang, Ke Tang, and Xin Yao. How Good Is Neural Combinatorial Optimization? A Systematic Evaluation on the Traveling Salesman Problem. <i>IEEE Computational Intelligence Magazine</i>, 2023, 18(3): 14-28. [<a href="https://ieeexplore.ieee.org/document/10188470">Paper</a>][<a href="https://arxiv.org/pdf/2209.10913.pdf">Arxiv</a>][<a href="https://github.com/yzhang-gh/benchmarking-tsp">Code</a>]</p>
</li>
<li><p>Zeyu Dai, <b>Shengcai Liu</b>*, Qing Li, and Ke Tang. Saliency Attack: Towards Imperceptible Black-box Adversarial Attack. <i>ACM Transactions on Intelligent Systems and Technology</i>, 2023, 14(3): 1-20. [<a href="https://dl.acm.org/doi/abs/10.1145/3582563">Paper</a>][<a href="https://arxiv.org/pdf/2206.01898.pdf">Arxiv</a>][<a href="https://github.com/Daizy97/SaliencyAttack">Code</a>]</p>
</li>
<li><p>Rui He, <b>Shengcai Liu</b>*, Shan He, and Ke Tang. Multi-Domain Active Learning: Literature Review and Comparative Study. <i>IEEE Transactions on Emerging Topics in Computational Intelligence</i>, 2023, 7(3): 791-804. [<a href="https://ieeexplore.ieee.org/document/9942709">Paper</a>][<a href="https://arxiv.org/pdf/2106.13516.pdf">Arxiv</a>]
</p>
</li>
<li><p><b>Shengcai Liu</b>, Ning Lu, Cheng Chen, and Ke Tang. Efficient Combinatorial Optimization for Word-level Adversarial Textual Attack. <i>IEEE/ACM Transactions on Audio, Speech and Language Processing</i>, 2022, 30: 98-111. [<a href="https://ieeexplore.ieee.org/document/9627642">Paper</a>][<a href="https://arxiv.org/pdf/2109.02229.pdf">Arxiv</a>][<a href="https://github.com/ColinLu50/NLP-Attack-LocalSearch">Code</a>]</p>
</li>
<li><p><b>Shengcai Liu</b>, Peng Yang, and Ke Tang. Approximately Optimal Construction of Parallel Algorithm Portfolios by Evolutionary Intelligence (in Chinese). <i>SCIENTIA SINICA Technologica</i>, 2023, 53(2): 280-290. [<a href="https://www.sciengine.com/doi/pdf/A261B46B9197412FA1DC6CF196A5750E">Paper</a>]</p>
</li>
<li><p><b>Shengcai Liu</b>, Ke Tang, and Xin Yao. Memetic Search for Vehicle Routing with Simultaneous Pickup-Delivery and Time Windows. <i>Swarm and Evolutionary Computation</i>, 66: 100927, 2021. [<a href="https://www.sciencedirect.com/science/article/abs/pii/S2210650221000882">Paper</a>][<a href="https://arxiv.org/pdf/2011.06331.pdf">Arxiv</a>][<a href="https://github.com/senshineL/VRPenstein">Code</a>]</p>
</li>
<li><p><b>Shengcai Liu</b>, Ke Tang, Peng Yang, and Xin Yao. Few-shots Parallel Algorithm Portfolio Construction via Co-evolution. <i>IEEE Transactions on Evolutionary Computation</i>, 2021, 25(3): 595-607. [<a href="https://ieeexplore.ieee.org/document/9354852/">Paper</a>] [<a href="https://arxiv.org/pdf/2007.00501.pdf">Arxiv</a>][<a href="https://github.com/senshineL/CEPS">Code</a>]</p>
</li>
<li><p><b>Shengcai Liu</b>, Ke Tang, and Xin Yao. Generative Adversarial Construction of Parallel Portfolios. <i>IEEE Transactions on Cybernetics</i>, 2022, 52(2): 784-795. [<a href="https://ieeexplore.ieee.org/abstract/document/9082022">Paper</a>]</p>
</li>
</ol>
<h3>Conference Papers</h3>
<ol>
<li><p>Ning Lu, <b>Shengcai Liu</b>*, Zhirui Zhang, Qi Wang, Haifeng Liu, and Ke Tang. Less is More: Understanding Word-level Textual Adversarial Attack via n-gram Frequency Descend. In: <i>Proceedings of The 2024 IEEE Conference on Artificial Intelligence (CAI’2024)</i>, Singapore, Singapore, 2024, 823-830. [<a href="https://ieeexplore.ieee.org/abstract/document/10605391">Paper</a>] [<a href="https://arxiv.org/pdf/2302.02568.pdf">Arxiv</a>]</p>
</li>
<li><p><b>Shengcai Liu</b>, Caishun Chen, Xinghua Qu, Ke Tang, and Yew-Soon Ong. Large Language Models as Evolutionary Optimizers. In: <i>Proceedings of The 2024 IEEE Congress on Evolutionary Computation (CEC’2024)</i>, Yokohama, Japan, 2024, 1-8. [<a href="https://ieeexplore.ieee.org/abstract/document/10611913">Paper</a>] [<a href="https://arxiv.org/pdf/2310.19046.pdf">Arxiv</a>]</p>
</li>
<li><p>Rui He, <b>Shengcai Liu</b>*, Jiahao Wu, Shan He, and Ke Tang. Multi-Domain Learning From Insufficient Annotations. In: <i>Proceedings of The 26th European Conference on Artificial Intelligence (ECAI’2023)</i>, Kraków, Poland, 2023, 1028-1035. [<a href="https://ebooks.iospress.nl/doi/10.3233/FAIA230375">Paper</a>][<a href="https://arxiv.org/pdf/2305.02757.pdf">Arxiv</a>]</p>
</li>
<li><p><b>Shengcai Liu</b>, Fu Peng, and Ke Tang. Reliable Robustness Evaluation via Automatically Constructed Attack Ensembles. In: <i>Proceedings of The 37th AAAI Conference on Artificial Intelligence (AAAI’2023)</i>, Washington, DC, 2023, 8852-8860. [<a href="https://ojs.aaai.org/index.php/AAAI/article/view/26064/25836">Paper</a>][<a href="https://arxiv.org/pdf/2211.12713.pdf">Arxiv</a>][<a href="https://github.com/LeegerPENG/AutoAE">Code</a>]</p>
</li>
<li><p>Fu Peng, <b>Shengcai Liu</b>*, and Ke Tang. Training Quantized Deep Neural Networks via
Cooperative Coevolution. In: <i>Proceedings of the 13th International Conference on Swarm Intelligence (ICSI’2022)</i>, Xi'an, China, 2022, 81-93. [<a href="https://arxiv.org/pdf/2112.14834.pdf">Paper</a>]</p>
</li>
<li><p>Kangfei Zhao, <b>Shengcai Liu</b>*, Yu Rong, and Jeffrey Xu Yu. Towards Feature-free TSP Solver Selection: A Deep Learning Approach. In: <i>Proceedings of the 20th International Joint Conference on Neural Networks (IJCNN’2021)</i>, Virtual Event, 2021, 1-8. [<a href="https://ieeexplore.ieee.org/abstract/document/9533538">Paper</a>][<a href="https://arxiv.org/pdf/2006.00715.pdf">Arxiv</a>][<a href="https://github.com/Kangfei/TSPSelector">Code</a>]</p>
</li>
<li><p><b>Shengcai Liu</b>, Ke Tang, and Xin Yao. On Performance Estimation in Automatic Algorithm Configuration. In: <i>Proceedings of The 34th AAAI Conference on Artificial Intelligence (AAAI’2020)</i>, New York, NY, 2020, 2384-2391. [<a href="https://ojs.aaai.org/index.php/AAAI/article/view/5618">Paper</a>]</p>
</li>
<li><p><b>Shengcai Liu</b>, Ke Tang, and Xin Yao. Automatic Construction of Parallel Portfolios via Explicit Instance Grouping. In: <i>Proceedings of The 33rd AAAI Conference on Artificial Intelligence (AAAI’2019)</i>, Honululu, HI, 2019, 1560-1567. [<a href="https://ojs.aaai.org/index.php/AAAI/article/view/3970">Paper</a>]</p>
</li>
<li><p><b>Shengcai Liu</b>, Yufan Wei, Ke Tang, A.K. Qin, and Xin Yao. Qos-aware Long-Term Based Service Composition in Cloud Computing. In: <i>Proceedings of The 14th IEEE Congress on Evolutionary Computation (CEC’2015)</i>, Sendai, Japan, 2015, 3362-3369. [<a href="https://ieeexplore.ieee.org/abstract/document/7257311">Paper</a>]</p>
</li>
</ol>
<h2>Grants</h2>
<ul>
<li><p>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.</p>
</li>
</ul>
<h2>Invited Talks</h2>
<ul>
<li><p>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.</p>
</li>
<li><p>Learn to Optimize @ The 2022 IEEE World Congress on Computational Intelligence (WCCI’2022). Jul 18, 2022. [<a href="http://senshineL.github.io/files/L2O_tutorial.pdf">Slides</a>]</p>
</li>
<li><p>Co-Evolved Parallel Algorithm Portfolios @ The 7th Workshop on Evolutionary Computation and Learning (ECOLE’2021). May 15, 2021.</p>
</li>
<li><p>Algorithm Portfolios for Beginners @ Magic-Data (数据魔术师). Apr 6, 2021. (online)</p>
</li>
</ul>
<h2>Teaching</h2>
<ul>
<li><p>CS110-Java程序设计基础 (for undergraduate students, 2024 Fall)</p>
</li>
</ul>
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