Dr Miqing Li

School of Computer Science
Associate Professor

Contact details

Telephone: +44 (0) 121 414 3712
Email: m.li.8@bham.ac.uk

Address: School of Computer Science
University of Birmingham
Edgbaston
Birmingham
B15 2TT
UK

Dr Miqing Li is an Associate Professor within the School of Computer Science at the University of Birmingham. His research is principally on multi-objective optimisation, where he focuses on developing population-based randomised algorithms (mainly evolutionary algorithms) for both general challenging problems (e.g. many-objective optimisation, constrained optimisation, robust optimisation, expensive optimisation) and specific challenging problems in other fields (e.g. software engineering, system engineering, product disassembly, post-disaster response, neural architecture search, reinforcement learning).

Miqing has published over 60 research papers in scientific journals and international conferences. Some of his papers, since published, have been amongst the most cited papers in corresponding journals such as IEEE Transactions on Evolutionary Computation, Artificial Intelligence, ACM Transactions on Software Engineering and Methodology, IEEE Transactions on Parallel and Distribution Systems

Please see Dr Miqing Li - personal web page to find out more about his work.

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Teaching

Algorithms for Data Science (MSc), autumn 2020, module lead.
Artificial Intelligence, (BSc, MSc), spring 2020, support.
Mathematical Foundation of Artificial Intelligence and Machine Learning (MSc), autumn 2020, support.
Artificial Intelligence II (BSc), spring 2021, support.

Research

Evolutionary multi-/many-objective optimisation --- algorithm design, performance assessment, archiving.
Evolutionary computation for other general challenging scenarios --- constraint handling, multi-modal optimisation, dynamic/robustness optimisation, data-driven optimisation.
Multi-criteria decision-making --- visualisation, objective reduction, assisted decision-making.
Search-based software engineering --- testing, software product line, software service composition
Engineering applications --- disassembly line balancing, post-disaster response, workflow scheduling in cloud computing.
Multi-objective optimisation for machine learning --- neural architecture search, reinforcement learning for video game.

Publications

Recent publications

Article

Tong, H, Li, M, Liu, J & Yao, X 2025, 'How do dynamic events change the fitness landscape of traveling salesman problems?', IEEE Transactions on Evolutionary Computation. https://doi.org/10.1109/TEVC.2025.3538547

Jiang, C & Li, M 2025, 'Multi-Objectivising Acquisition Functions in Bayesian Optimisation', ACM Transactions on Evolutionary Learning and Optimization. https://doi.org/10.1145/3716504

Bian, C, Zhou, Y, Li, M & Qian, C 2025, 'Stochastic population update can provably be helpful in multi-objective evolutionary algorithms', Artificial Intelligence, vol. 341, 104308. https://doi.org/10.1016/j.artint.2025.104308

Chen, T & Li, M 2024, 'Adapting Multi-objectivized Software Configuration Tuning', Proceedings of the ACM on Software Engineering, vol. 1, no. FSE, 25, pp. 539-561. https://doi.org/10.1145/3643751

Han, X, Chao, T, Yang, M & Li, M 2024, 'A steady-state weight adaptation method for decomposition-based evolutionary multi-objective optimisation', Swarm and Evolutionary Computation, vol. 89, 101641. https://doi.org/10.1016/j.swevo.2024.101641

Xiang, Y, Huang, H, Li, S, Li, M, Luo, C & Yang, X 2024, 'Automated test suite generation for software product lines based on quality-diversity optimisation', ACM Transactions on Software Engineering and Methodology, vol. 33, no. 2, 46, pp. 1–52. https://doi.org/10.1145/3628158

Chu, X, Han, X, Zhang, M & Li, M 2024, 'Improving decomposition-based MOEAs for combinatorial optimisation by intensifying corner weights', Swarm and Evolutionary Computation, vol. 91, 101722. https://doi.org/10.1016/j.swevo.2024.101722

Wang, Y, Zhen, L, Zhang, J, Li, M, Zhang, L, Wang, Z, Feng, Y, Xue, Y, Wang, X, Chen, Z, Luo, T, Mong Goh, RS & Liu, Y 2024, 'MedNAS: Multiscale Training-Free Neural Architecture Search for Medical Image Analysis', IEEE Transactions on Evolutionary Computation, vol. 28, no. 3, 10391077, pp. 668-681. https://doi.org/10.1109/TEVC.2024.3352641

Conference contribution

Ye, Y, Chen, T & Li, M 2025, Distilled Lifelong Self-Adaptation for Configurable Systems. in Proceedings of the ACM/IEEE 47th International Conference on Software Engineering (ICSE). Proceedings of the International Conference on Software Engineering, Association for Computing Machinery (ACM), 2025 IEEE/ACM 47th International Conference on Software Engineering (ICSE), Ottowa, Ontario, Canada, 26/04/25.

Zhang, Q, Li, M, Tang, K & Yao, X 2025, When is non-deteriorating population update in MOEAs beneficial? in H Singh, T Ray, J Knowles, X Li, J Branke, B Wang & A Oyama (eds), Evolutionary Multi-Criterion Optimization: 13th International Conference, EMO 2025, Canberra, ACT, Australia, March 4–7, 2025, Proceedings, Part I. 1 edn, Lecture Notes in Computer Science, vol. 15512, Springer, pp. 31-45, 13th International Conference on Evolutionary Multi-Criterion Optimization , Canberra, Australia, 4/03/25. https://doi.org/10.1007/978-981-96-3538-2_3

Ren, S, Bian, C, Li, M & Qian, C 2024, A first running time analysis of the strength Pareto evolutionary algorithm 2 (SPEA2). in Parallel Problem Solving from Nature – PPSN XVIII. Lecture Notes in Computer Science, Springer, 18th International Conference on Parallel Problem Solving From Nature PPSN 2024, Hagenberg, Austria, 14/09/24.

Bian, C, Ren, S, Li, M & Qian, C 2024, An archive can bring provable speed-ups in multi-objective evolutionary algorithms. in Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence (IJCAI-24).

Tan, Z, Yuan, B, Wang, H & Li, M 2024, A new step size update strategy for CMA-ES in multi-objective optimisation. in International Conference on Artificial Evolution (EA).

Li, M, Han, X, Chun, X & Liang, Z 2024, Empirical comparison between MOEAs and local search on multi-objective combinatorial optimisation problems. in Genetic and Evolutionary Computation Conference (GECCO).

Ren, S, Qiu, Z, Bian, C, Li, M & Qian, C 2024, Maintaining diversity provably helps in evolutionary multimodal optimization. in Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence (IJCAI-24).

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