Nan Li

Nan Li

Lecturer, Shanxi University

Selected Publications

*indicating the corresponding author/#indicating the equal contribution

Book

  1. Li Nan, Ma Lianbo, Qian Yuhua, Xue Bing, Zhang Mengjie. Performance Predictor inEvolutionary Neural Architecture Search: Methods and Applications[M]. Singapore:Springer, Hardcover ISBN:978-981-95-9154-1,2026. (The first academic monograph on performance predictors in evolutionary neural architecture search)

Journal/Conference Papers

  1. Li Nan, Ma Lianbo, Yu Guo, Xue Bing, Zhang Mengjie, Jin Yaochu. Survey on evolutionary deep learning: Principles, algorithms, applications, and open issues[J]. ACM Computing Surveys, 2024, 56(2): 1-34. (IF: 28.06, Highly Cited Paper (TOP 1% worldwide) and also Research Frontier Paper (ONLY one per issue))
  2. Li Nan, Bing Xue, Qi Chen, Lianbo Ma, Xingwei Wang, Min Huang, Mengjie Zhang. Evolutionary Zero-Shot Proxy in Various Evaluation Scenarios: A Symbolic Learning Perspective [J]. IEEE Transactions on Evolutionary Computation, 2025.
  3. Li Nan, Xue Bing, Ma Lianbo, Zhang Mengjie. Automatic Fuzzy Architecture Design for Defect Detection via Classifier-assisted Multiobjective Optimization Approach [J]. IEEE Transactions on Evolutionary Computation, 2024.
  4. Li Nan, Su Yonghui, Ma Lianbo. Efficient and Generalizable Mixed-Precision Quantization via Topological Entropy [C]. The Thirty-ninth Annual Conference on Neural Information Processing Systems, 2025. (CCF-A)
  5. Li Nan, Xue Bing, Ma Lianbo, Zhang Mengjie. Transferable Relativistic Predictor: Mitigating Cross-Task Cold-Start Issue in NAS [C]. International Joint Conference on Artificial Intelligence, 2025. (CCF-A).
  6. Ma Lianbo, Li Nan#, Zhu Peican, Tang Keke, Khan Asad, Wang Feng, Yu Guo. A Novel Fuzzy Neural Network Architecture Search Framework for Defect Recognition with Uncertainties [J] IEEE Transactions on Fuzzy Systems, 2024, doi: 10.1109/TFUZZ.2024.3373792 (Highly Cited Paper (TOP 1% worldwide) and also Research Frontier Paper (ONLY one per issue)).
  7. 李楠,贺美蕊,马连博.进化深度学习的研究现状与进展[J].信息与控制,2024,53(02):129-153. (入选科技期刊双语传播工程)