Distinguished Research Fellow
Shang Ke, PhD, Distinguished Research Fellow, Assistant Professor, and Doctoral Supervisor at Shenzhen University.
Email: shangk@szu.edu.cn or kshang@foxmail.com
PhD received from Xi'an Jiaotong University.
He has led one National Natural Science Foundation of China (NSFC) Youth Project and one NSFC General Project, as well as one Guangdong Provincial Natural Science Foundation General Project. He has published over 70 papers as first/corresponding author in important journals and international conferences such as IEEE TEVC, IEEE TCYB, IEEE CIM, IJCAI, PPSN, and GECCO, with over 1500 citations on Google Scholar. He received the Best Paper Award at ACM GECCO 2018/2021/2024, the runner-up Best Paper Award at IEEE CEC 2019, and a Best Paper nomination at PPSN 2020. He is currently a Senior Member of IEEE.
He plans to recruit 3 Master's students in 2025 (including an academic Master's degree in Computer Science and Technology and a professional Master's degree in Computer Technology). Students interested in artificial intelligence, large-scale models, and intelligent optimization algorithms are welcome to contact him. Research Capabilities and Achievements
Research Interests: Multi-objective optimization, computational intelligence, reinforcement learning, optimization based on large models, etc.
journal article
0. 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).
1. 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).
2. 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).
3. K. Shang#, W. Chen#, W. Liao, and H. Ishibuchi “HV-Net: Hypervolume Approximation based on DeepSets.” IEEE Transactions on Evolutionary Computation (2022). (#Equal Contribution)
4. 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).
5. 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
6. K. Shang, and H. Ishibuchi ""A New Hypervolume-based Evolutionary Algorithm for Many-objective Optimization."" IEEE Transactions on Evolutionary Computation (2020).
7. K. Shang, H. Ishibuchi, and X. Ni ""R2-based Hypervolume Contribution Approximation."" IEEE Transactions on Evolutionary Computation (2020).
8. K. Shang, Z. Feng, L. Ke, and F. T. Chan ""Comprehensive Pareto Efficiency in robust counterpart optimization."" Computers & Chemical Engineering (2016).
9. 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). (*Corresponding author)
10. Y. Nan, K. Shang, H. Ishibuchi, and L. He “An Improved Local Search Method for Large-Scale Hypervolume Subset Selection.” IEEE Transactions on Evolutionary Computation (2022).
11. L. He, K. Shang, Y. Nan, H. Ishibuchi, and D. Srinivasan “Relation Between Objective Space Normalization and Weight Vector Scaling in Decomposition-Based Multi-Objective Evolutionary Algorithms.” IEEE Transactions on Evolutionary Computation (2022).
12. L. He, K. Shang, and H. Ishibuchi ""Simultaneous Use of Two Normalization Methods in Decomposition-based Multi-objective Evolutionary Algorithms."" Applied Soft Computing (2020).
13. Y. Nan, K. Shang, H. Ishibuchi “Reverse Strategy for Non-dominated Archiving.” IEEE Access (2020).
14. L. M. Pang, H. Ishibuchi, and K. Shang ""Use of Two Penalty Values in Multi-objective Evolutionary Algorithm based on Decomposition."" IEEE Transactions on Cybernetics (2022).
15. L. M. Pang, H. Ishibuchi, and K. Shang ""Counterintuitive Experimental Results in Evolutionary Large-Scale Multi-Objective Optimization."" IEEE Transactions on Evolutionary Computation (2022).
16. H. Ishibuchi, L. M. Pang, and K. Shang ""Difficulties in Fair Performance Comparison of Multi-Objective Evolutionary Algorithms."" IEEE Computational Intelligence Magazine (2021).
17. W. Chen, H. Ishibuchi, and K. Shang “Fast Greedy Subset Selection from Large Candidate Solution Sets in Evolutionary Multi-objective Optimization”. IEEE Transactions on Evolutionary Computation (2021).
conference papers
0. T. Shu, K. Shang*, C. Gong, Y. Nan, and H. Ishibuchi, “Learning Pareto Set for Multi-Objective Continuous Robot Control.” IJCAI 2024.
1. K. Shang, W. Liao, and H. Ishibuchi “HVC-Net: Deep Learning based Hypervolume Contribution Approximation.” Parallel Problem Solving from Nature (PPSN2022).
2. K. Shang, H. Ishibuchi, L. M. Pang, and Y. Nan “Reference Point Specification for Greedy Hypervolume Subset Selection.” IEEE International Conference on Systems, Man, and Cybernetics (SMC2021).
3. 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
4. K. Shang, H. Ishibuchi, and Y. Nan “Distance-based Subset Selection Revisited”. Proceedings of the Genetic and Evolutionary Computation Conference. (GECCO2021).
5. K. Shang, H. Ishibuchi, L. Chen, W. Chen, and L. M. Pang “Improving the Efficiency of R2HCA-EMOA”. 11th International Conference on Evolutionary Multi-Criterion Optimization (EMO2021).
6. K. Shang, H. Ishibuchi, Y. Nan, and W. Chen “Transformation-based Hypervolume Indicator: A Framework for Desi
