πΌ Associate Professor, Associate Head of
Department
π« Huazhong University of Science and Technology (HUST)
β‘ IEEE Senior Member | π§ chenghehust [at] gmail.com
Dr. Cheng He is currently an Associate Professor with the School of Electrical and Electronic
Engineering,
Huazhong University of Science and Technology, China.
π Research Interests: Artificial/Computational Intelligence (including evolutionary
multi-objective optimization,
model-based optimization, large-scale optimization, etc.).
π Google Scholar / Github / LinkedIn
We have categorized our articles as surrogate-assisted optimization, large-scale optimization, multi-/many-objective optimization, and deep learning and its applications. You are welcome to cite these papers via the Bibtex .
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π Accelerating Large-scale Multiobjective Optimization via Problem Reformulation
Cheng He, Lianghao Li, Ye Tian, Xingyi Zhang, Ran Cheng, Yaochu Jin, Xing Yao, IEEE Transactions on Evolutionary Computation, 2019   |
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π Evolutionary Multiobjective Optimization Driven by Generative Adversarial Networks (GANs)
Cheng He, Shuhua Huang, Ran Cheng, Tan Kay Chen Yaochu Jin, IEEE Transactions on Cybernetics, 2021   paper code poster β¨ Highlights: GMOEA focuses on efficient offspring generation via learning from the distribution of promising solutions. GMOEA is capable of handling MOPs with up to 200 decision variables effectively, which is a new research direction for model-based evolutionary computation. |
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π Evolutionary Large-Scale Multiobjective Optimization for Ratio Error Estimation of Voltage
Transformers
Cheng He, Ran Cheng, Chuanji Zhang, Ye Tian, Qin Cheng Xin Yao, IEEE Transactions on Evolutionary Computation, 2020   paper code β¨ Highlights: TREE is a large-scale multiobjective optimization test suite extracted from the power delivery system, aiming at handling real-time ratio error estimation of voltage transformers. Generally, the maximum number of decision variables is up to half a million, and it includes constraints, which guide the design of a meaningful evolutionary algorithm. |
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π A Classification-Based Surrogate-Assisted Evolutionary Algorithm for Expensive Many-Objective
Optimization
Linqiang Pan, Cheng He, Ye Tian, Handing Wang, Xingyi Zhang, Yaochu Jin. IEEE Transactions on Evolutionary Computation, 2018   code β¨ Highlights: CSEA is a classification-based surrogate-assisted evolutionary algorithm, which uses an uncertainty configuration to balance between convergence and uncertainty. As an early attempt to use the classification model for capturing the dominance relationship in a reduced fitness landscape, which lies the foundation for future research on expensive high-dimensional optimization. |