Cheng He (何成)
Cheng He (何成)
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Dimension Dropout for Evolutionary High-Dimensional Expensive Multiobjective Optimization
In the past decades, a number of surrogate-assisted evolutionary algorithms (SAEAs) have been developed to solve expensive …
Jianqing Lin
,
Cheng He
,
Ran Cheng
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DOI
Large-Scale Multiobjective Optimization via Problem Decomposition and Reformulation
Lianghao Li
,
Cheng He
,
Ran Cheng
,
Linqiang Pan
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Manifold Learning Inspired Mating Restriction for Evolutionary Constrained Multiobjective Optimization
Lianghao Li
,
Cheng He
,
Ran Cheng
,
Linqiang Pan
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Multi-objective Neural Architecture Search with Almost No Training
In the recent past, neural architecture search (NAS) has attracted increasing attention from both academia and industries. Despite the …
Shengran Hu
,
Ran Cheng
,
Cheng He
,
Zhichao Lu
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DOI
Operator-Adapted Evolutionary Large-Scale Multiobjective Optimization for Voltage Transformer Ratio Error Estimation
Large-scale multiobjective optimization problems (LSMOPs) exist widely in real-world applications, and they are challenging for …
Changwu Huang
,
Lianghao Li
,
Cheng He
,
Ran Cheng
,
Xin Yao
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DOI
Population Sizing of Evolutionary Large-Scale Multiobjective Optimization
Large-scale multiobjective optimization problems (LSMOPs) are emerging and widely existed in real-world applications, which involve a …
Cheng He
,
Ran Cheng
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DOI
Iterated Problem Reformulation for Evolutionary Large-Scale Multiobjective Optimization
Due to the curse of dimensionality, two main issues remain challenging for applying evolutionary algorithms (EAs) to large-scale …
Cheng He
,
Ran Cheng
,
Ye Tian
,
Xingyi Zhang
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DOI
Efficient Evolutionary Deep Neural Architecture Search (NAS) by Noisy Network Morphism Mutation
Deep learning has achieved enormous breakthroughs in the field of image recognition. However, due to the time-consuming and error-prone …
Yiming Chen
,
Tianci Pan
,
Cheng He
,
Ran Cheng
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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
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DOI
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