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Ke Shang (尚可)

I am a Research Assistant Professor in Prof. Hisao Ishibuchi's group at the Department of Computer Science and Engineering, Southern University of Science and Technology (SUSTech).
My research interests include multi-objective optimization and artificial intelligence.

Email: kshang at foxmail dot com or shangk at sustech dot edu dot cn

Office: 650b, South Tower, CoE Building, SUSTech, Shenzhen, China

News

2022-8-23 Our paper “Learning to Approximate: Auto Direction Vector Set Generation for Hypervolume Contribution Approximation” has been conditionally accpeted by TEVC.
2022-8-23 I will give a talk for a seminar organized by IEEE CIS ACT chapter on 24 Aug. For more information [link].

Experiences

2005 - 2009, Xi’an Jiaotong University, Bachelor Degree
2009 - 2016, Xi’an Jiaotong University, PhD Degree
2012 - 2014, The University of Tokushima, Japan, Visiting Scholar
2016.10 - 2016.12, The Hong Kong Polytechnic University, Research Assistant
2017 - 2019, Southern University of Science and Technology, Postdoc Researcher
2019 - now, Southern University of Science and Technology, Research Assistant Professor

Publications

Journal papers

  • 14. Shang K., Shu T., and Ishibuchi H. “Learning to Approximate: Auto Direction Vector Set Generation for Hypervolume Contribution Approximation.” IEEE Transactions on Evolutionary Computation (conditionally accepted).
    13. He L., Shang K., Nan Y., Ishibuchi H., and Srinivasan D. “Relation Between Objective Space Normalization and Weight Vector Scaling in Decomposition-Based Multi-Objective Evolutionary Algorithms.” IEEE Transactions on Evolutionary Computation (2022).
    12. Pang L. M., Ishibuchi H., and Shang K. "Use of Two Penalty Values in Multi-objective Evolutionary Algorithm based on Decomposition." IEEE Transactions on Cybernetics (2022).
    11. Shang K.#, Chen W.#, Liao W., and Ishibuchi H. “HV-Net: Hypervolume Approximation based on DeepSets.” IEEE Transactions on Evolutionary Computation (2022). (#Equal Contribution)
    10. Pang L. M., Ishibuchi H., and Shang K. "Counterintuitive Experimental Results in Evolutionary Large-Scale Multi-Objective Optimization." IEEE Transactions on Evolutionary Computation (2022).
    9. Ishibuchi H., Pang L. M., and Shang K. "Difficulties in Fair Performance Comparison of Multi-Objective Evolutionary Algorithms." IEEE Computational Intelligence Magazine (2021).
    8. Shang K., Ishibuchi H., Chen W., Nan Y., and Liao W. “Hypervolume-Optimal μ-Distributions on Line/Plane-based Pareto Fronts in Three Dimensions”. IEEE Transactions on Evolutionary Computation (2021).
    7. Chen W., Ishibuchi H., and Shang K. “Fast Greedy Subset Selection from Large Candidate Solution Sets in Evolutionary Multi-objective Optimization”. IEEE Transactions on Evolutionary Computation (2021).
    6. Shang K., Ishibuchi H., He L., and Pang L. M. “A Survey on the Hypervolume Indicator in Evolutionary Multi-objective Optimization.” IEEE Transactions on Evolutionary Computation (2020).
    5. Shang K., and Ishibuchi H. "A New Hypervolume-based Evolutionary Algorithm for Many-objective Optimization." IEEE Transactions on Evolutionary Computation (2020).
    4. Shang K., Ishibuchi H., and Ni X. "R2-based Hypervolume Contribution Approximation." IEEE Transactions on Evolutionary Computation (2020).
    3. He L., Shang K., and Ishibuchi H. "Simultaneous Use of Two Normalization Methods in Decomposition-based Multi-objective Evolutionary Algorithms." Applied Soft Computing (2020).
    2. Nan Y., Shang K., Ishibuchi H. “Reverse Strategy for Non-dominated Archiving.” IEEE Access (2020).
    1. Shang K., Feng Z., Ke L., and Chan F. T. "Comprehensive Pareto Efficiency in robust counterpart optimization." Computers & Chemical Engineering (2016).

    Conference papers

    15. Shu T., Shang K., Nan Y., and Ishibuchi H. “Direction Vector Selection for R2-based Hypervolume Contribution Approximation.” Parallel Problem Solving from Nature (PPSN2022).
    14. Shang K., Liao W., and Ishibuchi H. “HVC-Net: Deep Learning based Hypervolume Contribution Approximation.” Parallel Problem Solving from Nature (PPSN2022).
    13. Shang K., Ishibuchi H., Pang L. M., and Nan Y. “Reference Point Specification for Greedy Hypervolume Subset Selection.” IEEE International Conference on Systems, Man, and Cybernetics (SMC2021).
    12. Pang L. M.#, Shang K.#, Chen L., Ishibuchi H., and Chen W. “Proposal of a New Test Problem for Large-Scale Many-Objective Optimization”. IEEE International Conference on Systems, Man, and Cybernetics (SMC2021). (#Equal Contribution)
    11. Nan Y., Shang K., Ishibuchi H., and He L. “Improving Hypervolume-based Greedy Sequential Insertion Subset Selection in Evolutionary Multi-objective Optimization”. IEEE International Conference on Systems, Man, and Cybernetics (SMC2021).
    10. Nan Y., Shang K., Ishibuchi H., and He L. “A Two Stage Hypervolume Contribution Approximation Method Based on R2 Indicator”. IEEE Congress on Evolutionary Computation (CEC2021).
    9. Shang K., Ishibuchi H., and Chen W. “Greedy Approximated Hypervolume Subset Selection for Many-objective Optimization”. Proceedings of the Genetic and Evolutionary Computation Conference. (GECCO2021). Best Paper Award
    8. Shang K., Ishibuchi H., and Nan Y. “Distance-based Subset Selection Revisited”. Proceedings of the Genetic and Evolutionary Computation Conference. (GECCO2021).
    7. Shang K., Ishibuchi H., Chen L., Chen W., and Pang L. M. “Improving the Efficiency of R2HCA-EMOA”. 11th International Conference on Evolutionary Multi-Criterion Optimization (EMO2021).
    6. Shang K., Ishibuchi H., Nan Y., and Chen W. “Transformation-based Hypervolume Indicator: A Framework for Designing Hypervolume Variants”. IEEE Symposium Series on Computational Intelligence (SSCI2020).
    5. Shang K., Ishibuchi H., Chen W., and Adam L. "Hypervolume optimal mu-distributions on line-based Pareto fronts in three dimensions." Parallel Problem Solving from Nature. (PPSN2020).
    4. Chen W., Ishibuchi H., and Shang K. “Proposal of a realistic many-objective test suite.” Parallel Problem Sovling from Nature. (PPSN2020). Best Paper Nomination
    3. Nan Y.#, Shang K.#, and Ishibuchi H. "What is a Good Direction Vector Set for the R2-based Hypervolume Contribution Approximation." Proceedings of the Genetic and Evolutionary Computation Conference. (GECCO2020). (#Equal Contribution)
    2. Ishibuchi H., Peng Y., and Shang K. "A Scalable Multimodal Multiobjective Test Problem." IEEE Congress on Evolutionary Computation (CEC2019). First Runner-up Conference Paper Award
    1. Shang K., Ishibuchi H., Zhang M. L., and Liu Y. "A new R2 indicator for better hypervolume approximation." Proceedings of the Genetic and Evolutionary Computation Conference. (GECCO2018). Best Paper Award

Patents

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. 实审. 中国. 202210084970.9. 2022/1/25. 南方科技大学.

Invited Talks

1. 演化多目标优化的发展现状及最新研究进展, 中国计算机学会青年计算机科技论坛兰州分论坛, 2020年7月4日. [link]
2. Hypervolume Approximation for Many-objective Optimization, IEEE CIS Seminar, 2022-8-24. [link]

Tutorials

1. Difficulties in Fair Performance Comparison of Multiobjective Evolutionary Algorithms, GECCO 2022 Tutorial. [link]
2. How to Compare Evolutionary Multi-Objective Optimization Algorithms: Parameter Specifications, Indicators and Test Problems. WCCI 2022 Tutorial. [link]

Awards

1. 2021 GECCO Best Paper Award (Shang K., Ishibuchi H., Chen W.)
2. 2021 Overseas High-Caliber Personnel in Shenzhen
3. 2020 IEEE CIS Travel Grant
4. 2020 PPSN Best Paper Nomination (Chen W., Ishibuchi H., Shang K.)
5. 2019 CEC First Runner-up Conference Paper Award (Ishibuchi H., Peng Y., Shang K.)
6. 2018 GECCO Best Paper Award (Shang K., Ishibuchi H., Zhang M. L., Liu Y.)

Grants

1. 2021.01-2023.12 Youth Program of NSFC, PI
2. 2020.03-2023.02 Shenzhen postdoctoral research funding, PI