Selected Publications

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Research

• Romera-Paredes B, Barekatain M, Novikov A, et al. Mathematical discoveries from program search with large language models[J]. Nature, 2024, 625(7995): 468-475.
• Prasad A, Hase P, Zhou X, et al. Grips: Gradient-free, edit-based instruction search for prompting large language models[J]. arXiv preprint arXiv:2203.07281, 2022.
• Zhou Y, Wu X, Wu J, et al. HM3: Hierarchical Multi-Objective Model Merging for Pretrained Models[J]. arXiv preprint arXiv:2409.18893, 2024.
• Fernando C, Banarse D, Michalewski H, et al. Promptbreeder: Self-referential self-improvement via prompt evolution[J]. arXiv preprint arXiv:2309.16797, 2023.
• Liu L, Zhang C, Wu L, et al. Instruct-of-Reflection: Enhancing Large Language Models Iterative Reflection Capabilities via Dynamic-Meta Instruction[C]//Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025: 9956-9978.
• Nasir, Muhammad Umair, et al. “LLMatic: neural architecture search via large language models and quality diversity optimization.” proceedings of the Genetic and Evolutionary Computation Conference. 2024.
• Akiba T, Shing M, Tang Y, et al. Evolutionary optimization of model merging recipes[J]. Nature Machine Intelligence, 2025: 1-10.
• Li H, Ai Q, Chen J, et al. Blade: Enhancing black-box large language models with small domain-specific models[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2025, 39(23): 24422-24430.
• Beichen Huang, Xingyu Wu, Yu Zhou, Jibin Wu, Liang Feng, Ran Cheng, Kay Chen Tan. Evaluation of Large Language Models as Solution Generators in Black-Box Optimization. IEEE Computational Intelligence Magazine, 2025.
• Yuxiao Huang, Wenjie Zhang, Liang Feng, Xingyu Wu, Kay Chen Tan. How multimodal integration boost the performance of llm for optimization: Case study on capacitated vehicle routing problems, The 2025 IEEE Symposium Series on Computational Intelligence (SSCI’25), 17 – 20 March, 2025, Trondheim, Norway.
• Xun Zhou, Xingyu Wu, Liang Feng, Zhichao Lu, Kay Chen Tan. Design Principle Transfer in Neural Architecture Search via Large Language Models. The 39th AAAI Conference on Artificial Intelligence (AAAI’25), 2025.
• Liu S, Chen C, Qu X, et al. Large language models as evolutionary optimizers[C]//2024 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2024: 1-8.
• Ma Z, Guo H, Gong Y J, et al. Toward automated algorithm design: A survey and practical guide to meta-black-box-optimization[J]. IEEE Transactions on Evolutionary Computation, 2025.
• Ye H, Wang J, Cao Z, et al. Reevo: Large language models as hyper-heuristics with reflective evolution[J]. arXiv preprint arXiv:2402.01145, 2024.
• Y. Jiang, R. Yan, X. Yao, B. Chen, and B. Yuan, “Hexgen: Generative inference of foundation model over heterogeneous decentralized environment,” 2023, arXiv:2311.11514.
• S. Sudhakaran, M. González-Duque, C. Glanois, M. Freiberger, E. Najarro, and S. Risi, “Prompt-guided level generation,” in Proc. Companion Conf. Genet. Evol. Comput., 2023, pp. 179–182.
• P. Maddigan, A. Lensen, and B. Xue, “Explaining genetic programming trees using large language models,” 2024, arXiv:2403.03397.
• S. Shyam, G.-D. Miguel, F. Matthias, G. Claire, N. Elias, and R. Sebastian, “MarioGPT: Open-ended Text2Level generation through large language models,” 2023, arXiv:2302.05981
• Xingyu Wu, Yan Zhong, Jibin Wu, Bingbing Jiang, Kay Chen Tan. Large Language Model-Enhanced Algorithm Selection: Towards Comprehensive Algorithm Representation. The 33rd International Joint Conference on Artificial Intelligence (IJCAI’24, Oral), August 3-9, 2024, Jeju, South Korea.
• Huang, Yuxiao, et al. “How multimodal integration boost the performance of llm for optimization: Case study on capacitated vehicle routing problems.” 2025 IEEE Symposium for Multidisciplinary Computational Intelligence Incubators (MCII). IEEE, 2025.
• Huang, Y., Wu, S., Zhang, W., Wu, J., Feng, L., & Tan, K. C. (2025). Autonomous multi-objective optimization using large language model. IEEE Transactions on Evolutionary Computation.
• Ponce D, Etchegoyhen T, Del Ser J. GeLaCo: An Evolutionary Approach to Layer Compression[J]. arXiv preprint arXiv:2507.10059, 2025.

Special Issue

• IEEE Transactions on Evolutionary Computation, Special Issue on Evolutionary Computation Meets Large Language Models [Link]