博士，深圳大学特聘研究员、助理教授、博导。
本科博士毕业于西安交通大学。
主持国家自然科学基金青年基金项目1项、面上项目1项，广东省自然科学基金面上项目1项，中兴通讯产学研合作项目1项，以第一/通讯作者在IEEE TEVC、IEEE TCYB、IEEE CIM、IJCAI、PPSN、GECCO等重要期刊和国际会议发表论文100余篇，谷歌学术引用2000余次，荣获ACM GECCO2018/2021/2024最佳论文奖、IEEE CEC2019最佳论文奖亚军、PPSN2020、GECCO2025最佳论文提名，现为IEEE高级会员，Memetic Computing编委。
研究兴趣：进化计算、多目标优化、大模型算法设计、自进化智能体、开放式进化等。
期刊论文 (*Corresponding author)
1. K. Shang, H. Ishibuchi, Z. Zhu, and Q. Zhang, “An efficient evolutionary algorithm for few-for-many optimization,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 6, pp. 1362–1377, Jun. 2026. doi: 10.1109/JAS.2026.125852
2. K. Shang, Y. Wang, Z. Zhu, and H. Ishibuchi. "Enhancing Pareto Set Learning with evolutionary multi-objective optimization." Swarm and Evolutionary Computation103 (2026): 102346.
3. K. Shang, G. Wu, L. M. Pang, and H. Ishibuchi “Targeted Pareto Optimization for Subset Selection with Monotone Objective Function and Cardinality Constraint.” IEEE Transactions on Evolutionary Computation (2024).
4. K. Shang, T. Shu, H. Ishibuchi, Y. Nan, and L. M. Pang “Benchmarking Large-Scale Subset Selection in Evolutionary Multi-Objective Optimization.” Information Sciences (2022).
5. K. Shang, T. Shu, and H. Ishibuchi “Learning to Approximate: Auto Direction Vector Set Generation for Hypervolume Contribution Approximation.” IEEE Transactions on Evolutionary Computation (2022).
6. K. Shang#, W. Chen#, W. Liao, and H. Ishibuchi “HV-Net: Hypervolume Approximation based on DeepSets.” IEEE Transactions on Evolutionary Computation (2022). (#Equal Contribution)
7. K. Shang, H. Ishibuchi, W. Chen, Y. Nan, and W. Liao “Hypervolume-Optimal μ-Distributions on Line/Plane-based Pareto Fronts in Three Dimensions”. IEEE Transactions on Evolutionary Computation (2021).
8. K. Shang, H. Ishibuchi, L. He, and L. M. Pang “A Survey on the Hypervolume Indicator in Evolutionary Multi-objective Optimization.” IEEE Transactions on Evolutionary Computation (2020). ESI Highly Cited Paper 🏆
9. K. Shang, and H. Ishibuchi ""A New Hypervolume-based Evolutionary Algorithm for Many-objective Optimization."" IEEE Transactions on Evolutionary Computation (2020).
10. K. Shang, H. Ishibuchi, and X. Ni ""R2-based Hypervolume Contribution Approximation."" IEEE Transactions on Evolutionary Computation (2020).
11. K. Shang, Z. Feng, L. Ke, and F. T. Chan ""Comprehensive Pareto Efficiency in robust counterpart optimization."" Computers & Chemical Engineering (2016).
12. T. Shu, K. Shang*, H. Ishibuchi*, and Y. Nan “Effects of Archive Size on Computation Time and Solution Quality for Multi-Objective Optimization.” IEEE Transactions on Evolutionary Computation (2022).
会议论文 (*Corresponding author)
1. K. Shang, Z. Xiao, Y. Liu, J. Li, S. Wang, W. Sun. “A Scalable Benchmark Test Suite for Dynamic Multi-Objective Optimization with a Changing Number of Objectives”. PPSN 2026.
2. Y. Liu, H. Xu, D. Wei, Z. Xiao, K. Shang*. “Designing Hypervolume Subset Selection Algorithms with Large Language Models”. PPSN 2026.
3. Y. Wang, K. Shang*, and Zexuan Zhu. "Evolution Assisted Pareto Set Learning for Multi-Modal Multi-Objective Optimization." In 2025 International Conference on Machine Intelligence and Nature-Inspired Computing (MIND), pp. 237-242. IEEE, 2025. Best Student Paper Award 🏆
4. K. Zhang, A. E. Rodriguez-Fernandez, K. Shang*, H. Ishibuchi, and O. Schütze. "Hypervolume Gradient Subspace Approximation." PPSN 2024.
5. T. Shu, K. Shang*, C. Gong, Y. Nan, and H. Ishibuchi, “Learning Pareto Set for Multi-Objective Continuous Robot Control.” IJCAI 2024.
6. K. Shang, W. Liao, and H. Ishibuchi “HVC-Net: Deep Learning based Hypervolume Contribution Approximation.” Parallel Problem Solving from Nature (PPSN2022).
7. K. Shang, H. Ishibuchi, and W. Chen “Greedy Approximated Hypervolume Subset Selection for Many-objective Optimization”. Proceedings of the Genetic and Evolutionary Computation Conference. (GECCO2021). Best Paper Award 🏆
8. K. Shang, H. Ishibuchi, and Y. Nan “Distance-based Subset Selection Revisited”. Proceedings of the Genetic and Evolutionary Computation Conference. (GECCO2021).
9. K. Shang, H. Ishibuchi, W. Chen, and L. Adam ""Hypervolume optimal mu-distributions on line-based Pareto fronts in three dimensions."" Parallel Problem Solving from Nature. (PPSN2020).
10. K. Shang, H. Ishibuchi, M. L. Zhang, and Y. Liu ""A new R2 indicator for better hypervolume approximation."" Proceedings of the Genetic and Evolutionary Computation Conference. (GECCO2018). Best Paper Award 🏆
11. H. Zhu, K. Shang*, H. Ishibuchi* “STHV-Net: Hypervolume Approximation based on Set Transformer.” Proceedings of the Genetic and Evolutionary Computation Conference. (GECCO2023).
12. T. Shu, K. Shang*, Y. Nan, and H. Ishibuchi* “Direction Vector Selection for R2-based Hypervolume Contribution Approximation.” Parallel Problem Solving from Nature (PPSN2022).
13. W. Chen, H. Ishibuchi, and K. Shang “Proposal of a realistic many-objective test suite.” Parallel Problem Sovling from Nature. (PPSN2020). Best Paper Nomination 🏆
14. H. Ishibuchi, Y. Peng, and K. Shang ""A Scalable Multimodal Multiobjective Test Problem."" IEEE Congress on Evolutionary Computation (CEC2019). First Runner-up Conference Paper Award 🏆
专利
尚可, 石渕久生. 飞行决策生成方法和装置、计算机设备、存储介质. 发明. 授权. 中国. 202210084970.9. 2022/1/25
项目情况
1. 2021.01-2023.12 国家自然科学基金青年项目, 超体积指标在演化多目标优化算法中的关键问题研究, 主持
2. 2025.01-2028.12 国家自然科学基金面上项目, 进化辅助的Pareto解集学习算法及应用, 主持
3. 2025.01-2027.12 广东省自然科学基金面上项目，基于大模型的超体积子集选择算法研究，主持
4. 2025.09-2026.08 中兴通讯产学研合作项目，通用多目标算法框架技术研究，主持
获奖情况
2025年获得MIND国际会议最佳学生论文奖
2025年获得GECCO最佳论文提名
2024年获得全国工业互联网创新大赛二等奖
2024年获得GECCO最佳论文奖
2022年评为IEEE高级会员
2021年获得GECCO最佳论文奖
2020年获得PPSN最佳论文提名
2019年获得CEC最佳论文奖第二名
2018年获得GECCO最佳论文奖
团队成员：
博士生：
高宇（2025级）
谭致文（2026级）
硕士生：
王宇（2024级）
刘亚军（2025级）
肖志云（2025级）
吴啸宇（2026级）
