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Yanan Sun
Researcher at Sichuan University
Publications - 95
Citations - 3295
Yanan Sun is an academic researcher from Sichuan University. The author has contributed to research in topics: Convolutional neural network & Evolutionary computation. The author has an hindex of 20, co-authored 66 publications receiving 1635 citations. Previous affiliations of Yanan Sun include Sichuan Agricultural University & Victoria University of Wellington.
Papers
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Automatically Designing CNN Architectures Using the Genetic Algorithm for Image Classification
TL;DR: This article proposes an automatic CNN architecture design method by using genetic algorithms, to effectively address the image classification tasks and shows the very comparable classification accuracy to the best one from manually designed and automatic + manually tuning CNNs, while consuming fewer computational resources.
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Evolving Deep Convolutional Neural Networks for Image Classification
TL;DR: In this paper, an efficient variable-length gene encoding strategy is designed to represent the different building blocks and the potentially optimal depth in convolutional neural networks, which is expected to avoid networks getting stuck into local minimum that is typically a major issue in backward gradient-based optimization.
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IGD Indicator-Based Evolutionary Algorithm for Many-Objective Optimization Problems
Yanan Sun,Gary G. Yen,Zhang Yi +2 more
TL;DR: An IGD indicator-based evolutionary algorithm for solving many-objective optimization problems (MaOPs) is proposed and experimental results measured by the chosen performance metrics indicate that the proposed algorithm is very competitive in addressing MaOPs.
Posted Content
Evolving Deep Convolutional Neural Networks for Image Classification
TL;DR: A new method using genetic algorithms for evolving the architectures and connection weight initialization values of a deep convolutional neural network to address image classification problems and a novel fitness evaluation method is proposed to speed up the heuristic search with substantially less computational resource.
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Completely Automated CNN Architecture Design Based on Blocks
TL;DR: Experimental results show that the proposed algorithm outperforms the state-of-the-art CNNs hand-crafted and the CNNs designed by automatic peer competitors in terms of the classification performance and achieves a competitive classification accuracy against semiautomatic peer competitors.