Cheng He (何成)
Cheng He (何成)
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"Optimization"
Evolutionary Multiobjective Optimization Driven by Generative Adversarial Networks (GANs)
Recently, increasing works have been proposed to drive evolutionary algorithms using machine-learning models. Usually, the performance …
Cheng He
,
Shihua Huang
,
Ran Cheng
,
Kay Chen Tan
,
Yaochu Jin
Cite
DOI
Manifold Learning-Inspired Mating Restriction for Evolutionary Multiobjective Optimization With Complicated Pareto Sets
Under certain smoothness assumptions, the Pareto set of a continuous multiobjective optimization problem is a piecewise continuous …
Linqiang Pan
,
Lianghao Li
,
Ran Cheng
,
Cheng He
,
Kay Chen Tan
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DOI
Paired Offspring Generation for Constrained Large-Scale Multiobjective Optimization
Constrained multiobjective optimization problems (CMOPs) widely exist in real-world applications, and they are challenging for …
Cheng He
,
Ran Cheng
,
Ye Tian
,
Xingyi Zhang
,
Kay Chen Tan
,
Yaochu Jin
Cite
DOI
RelativeNAS: Relative Neural Architecture Search via Slow-Fast Learning
Despite the remarkable successes of convolutional neural networks (CNNs) in computer vision, it is time-consuming and error-prone to …
Hao Tan
,
Ran Cheng
,
Shihua Huang
,
Cheng He
,
Changxiao Qiu
,
Fan Yang
,
Ping Luo
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DOI
Evolutionary Large-Scale Multiobjective Optimization for Ratio Error Estimation of Voltage Transformers
Ratio error (RE) estimation of the voltage transformers (VTs) plays an important role in modern power delivery systems. Existing RE …
Cheng He
,
Ran Cheng
,
Chuanji Zhang
,
Ye Tian
,
Qin Chen
,
Xin Yao
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DOI
Guiding Evolutionary Multiobjective Optimization With Generic Front Modeling
In evolutionary multiobjective optimization, the Pareto front (PF) is approximated by using a set of representative candidate solutions …
Ye Tian
,
Xingyi Zhang
,
Ran Cheng
,
Cheng He
,
Yaochu Jin
Cite
DOI
A Hybrid Surrogate-Assisted Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization
Many real-world optimization problems are challenging because the evaluation of solutions is computationally expensive. As a result, …
Kanzhen Wan
,
Cheng He
,
Auraham Camacho
,
Ke Shang
,
Ran Cheng
,
Hisao Ishibuchi
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DOI
Surrogate-Assisted Expensive Many-Objective Optimization by Model Fusion
Surrogate-assisted evolutionary algorithms have played an important role in expensive optimization where a small number of …
Cheng He
,
Ran Cheng
,
Yaochu Jin
,
Xin Yao
Cite
DOI
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