Cheng He (何成)

Name: Cheng He
Position: Research Assistant Professor
Institution: Department of Computer Science and Engineering, SUSTech
Research Area:  Model-based/data-driven optimization,

multi/many-objective optimization,

large-scale optimization,

combination of deep learning and

evolutionary algorithm, etc.

Contact: chenghehust@gmail.com

 

何成博士现任南方科技大学计算机科学与工程系研究助理教授;曾任南方科技大学计算机与工程系博士后(2018-2020)。何成博士的研究领域为计算智能(具体包括演化多目标优化、模型辅助优化、大规模优化等),近5年共发表论文20余篇(含JCR Q1期刊长文共14篇,其中IEEE TEVC和IEEE TCYB共7篇),获批发明专利4项,国家自然科学基金青年基金一项;将研究成果应用于深度学习模型优化、电网故障检测等重要工程与科研领域。
何成博士曾担任2017 BIC-TA国际会议出版主席,2019 IEEE CEC数据驱动的多目标在线优化竞赛共同主席,2019、2020 IEEE SSCI会议Model-Based Evolutionary Algorithms 分会共同主席;获得2019年 BIC-TA国际会议最佳论文奖,2020年南方科技大学卓越博士后奖。

Dr. Cheng He is currently a Research Assistant Professor with the Department of Computer Science and Engineering, Southern University of Science and Technology, China.  Previously, he was a postdoctoral research fellow (2018-2020) with the Department of Computer Science and Engineering, Southern University of Science and Technology. He received his Ph.D. degree from Huazhong University of Science and Technology in 2018. His main research interests are Artificial/Computational Intelligence (includes evolutionary multi-objective optimization, model-based optimization, large-scale optimization, etc.). He serves as a Publication Chair for the12th International Conference on Bio-inspired Computing: Theories and Applications (2017), and a Co-Chair for Competition on Online Data-Driven Multi-Objective Optimization (2019), and a Co-Chair for IEEE Symposium on Model-Based Evolutionary Algorithms (2019 and 2020). He received the Best Paper Award of  The 14th International Conference on Bio-inspired Computing: Theories and Applications (BIC-TA 2019), and the 6th Presidential Outstanding Postdoctoral Award of SUSTech (2020).

Interest

  • Model-based/data-driven optimization, multi/many-objective optimization, large-scale optimization, the combination of deep learning and evolutionary algorithm, and real-world problems.

Research Grants

  • 2020 – 2022: Computationally Expensive Large-Scale Multi-Objective Optimization Driven by Generative Learning, PI, 160,000 RMB, National Science Foundation, China.

Awards

  • 2020: SUSTech the 6th Presidential Outstanding Postdoctoral Award 
  • 2019: The 14th International Conference on Bio-inspired Computing: Theories and Applications (BIC-TA 2019) Best Paper Award, China.

Publication

1. Journal Papers

  1. Yanguo Kong*, Xiangyi Kong*, Cheng He, Changsong Liu, Liting Wang, Lijuan Su, Jun Gao, Qi Guo, and Ran Cheng*. Constructing an Automatic Diagnosis and Severity-Classification Model for Acromegaly Using Facial Photographs by Deep Learning. Journal of Hematology & Oncology,13(1): 1-4, 2020.
  2. Linqiang Pan, Wenting Xu, Lianghao Li, Cheng He*, and Ran Cheng*. Adaptive Simulated Binary Crossover for Rotated Multi-Objective Optimization. Swarm and Evolutionary Computation, in Press.
  3. Cheng He, Ran Cheng*, and Danial Yazdani. Adaptive Offspring Generation for Evolutionary Large-Scale Multiobjective Optimization. IEEE Transactions on Systems, Man and Cybernetics: Systems, 2020.
  4. Zhanglu Hou, Cheng He and Ran Cheng*. Reformulating Preferences into Constraints for Evolutionary Multi- and Many-Objective Optimization. Information Sciences, 2020.
  5. Cheng He, Shihua Huang, Ran Cheng*, Kay Chen Tan, and Yaochu Jin. Evolutionary Multiobjective Optimization Driven by Generative Adversarial Networks (GANs). IEEE Transactions on Cybernetics, 2020.
  6. Cheng He, Ran Cheng*, Chuanji Zhang, Ye Tian, Qin Chen, and Xin Yao. Evolutionary Large-Scale Multiobjective Optimization for Ratio Error Estimation of Voltage Transformers. IEEE Transactions on Evolutionary Computation, 2020.
  7. Linqiang Pan, Lianghao Li, Ran Cheng, Cheng He*, and Kay Chen Tan. Manifold Learning Inspired Mating Restriction for Evolutionary Multi-Objective Optimization with Complicated Pareto Sets. IEEE Transactions on Cybernetics, 2020.
  8. Cheng He, Ye Tian, Handing Wang, and Yaochu Jin. A Repository of Real-World Datasets for Data-Driven Evolutionary Multiobjective Optimization. Complex & Intelligent Systems, 6, 189-197, 2020.
  9. Ye Tian, Xingyi Zhang*, Ran Cheng*, Cheng He, and Yaochu Jin. Guiding Evolutionary Multiobjective Optimization with Generic Front Modeling. IEEE Transactions on Cybernetics, 50 (3), 1106-1119, 2020.
  10. Ye Tian, Cheng He, Ran Cheng, Xingyi Zhang. A Multi-Stage Evolutionary Algorithm for Better Diversity Preservation in Multi-Objective Optimization. IEEE Transactions on Systems, Man and Cybernetics: Systems, 2019.
  11. Linqiang Pan, Lianghao Li, Cheng He*, and Kay Chen Tan. A Subregion Division-Based Evolutionary Algorithm with Effective Mating Selection for Many-Objective Optimization. IEEE Transactions on Cybernetics, 50(8), 3477-3490, 2019.
  12. Cheng He, Lianghao Li, Ye Tian, Xingyi Zhang, Ran Cheng, Yaochu Jin, and Xin Yao. Accelerating Large-scale Multiobjective Optimization via Problem Reformulation. IEEE Transactions on Evolutionary Computation, 23(6), 949-961, 2019.
  13. Cheng He, Zhixiong Zhang, Jie Ye, Jinbang Xu, and Linqiang Pan*. Switching Ripple Suppressor Design of the Grid-Connected Inverters: A Perspective of Many-Objective Optimization with Constraints Handling. Swarm and Evolutionary Computation, 44, 293-303, 2019.
  14. Ran Cheng*, Cheng He, Yaochu Jin, Xin Yao. Model-based evolutionary algorithms: a short survey. Complex & Intelligent Systems, 4(4), 283-292, 2018.
  15. Wenbo Dong, Kang Zhou, Huaqing Qi, Cheng He, Jun Zhang*. A Tissue P System Based Evolutionary Algorithm for Multi-Objective VRPTW. Swarm and Evolutionary Computation, 39, 310-322, 2018.
  16. Linqiang Pan, Cheng He, Ye Tian, Handing Wang, Xingyi Zhang, and Yaochu Jin*. A Classification-Based Surrogate-Assisted Evolutionary Algorithm for Expensive Many-Objective Optimization. IEEE Transactions on Evolutionary Computation, 23(1), 74-88, 2019.
  17. Cheng He, Ye Tian, Yaochu Jin, Xingyi Zhang, and Linqiang Pan*. A Radial Space Division Based Evolutionary Algorithm for Many-Objective Optimization. Applied Soft Computing, 61, 603-621, 2017.
  18. Linqiang Pan, Cheng He, Ye Tian, Yansen Su, and Xingyi Zhang*. A Region Division Based Diversity Maintaining Approach for Many-Objective Optimization. Integrated Computer-Aided Engineering, 24(3), 279-296, 2017.
  19. Zhihua Chen, Cheng He, Ying Zheng, Xiaolong Shi, and Tao Song*. A Novel Thermodynamic Model and Temperature Control Method of Laser Soldering Systems. Mathematical Problems in Engineering, 2015, 2015.

2. Conference Papers

  1. Cheng He, Ran Cheng*, Ye Tian, and Xingyi Zhang. Iterated Problem Reformulation for Evolutionary Large-Scale Multiobjective Optimization. IEEE Congress on Evolutionary Computation (CEC), 2020, accepted.
  2. Yiming Chen, Tianci Pan, Cheng He*, and Ran Cheng*. Efficient Evolutionary Deep Neural Architecture Search (NAS) by Noisy Network Morphism Mutation. International Conference on Bio-Inspired Computing: Theories and Applications. Springer, Singapore, 2019: 761-769.
  3. Hao Tan, Cheng He*, Dexuan Tang, and Ran Cheng*. Efficient Evolutionary Neural Architecture Search (NAS) by Modular Inheritable Crossover. International Conference on Bio-Inspired Computing: Theories and Applications. Springer, Singapore, 2019: 497-508.
  4. Kanzhen Wan, Cheng He, Auraham Camacho, Ke Shang, Ran Cheng, and Hisao Ishibuchi*. A Hybrid Surrogate-Assisted Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization. IEEE Congress on Evolutionary Computation(CEC), 2018-2025, 2019.
  5. Cheng He, Ran Cheng*, Yaochu Jin, and Xin Yao. Surrogate-Assisted Expensive Many-Objective Optimization by Model Fusion. IEEE Congress on Evolutionary Computation (CEC), 1672-1679, 2019.
  6. Cheng He, Linqiang Pan*, Hang Xu, Ye Tian, and Xingyi Zhang. An Improved Reference Point Sampling Method on Pareto Optimal Front. IEEE Congress on Evolutionary Computation (CEC), 5230-5237, 2016.