N. H. Luong, Q. M. Phan, A. Vo, T. N. Pham, and D. T. Bui, “Lightweight multi-objective evolutionary neural architecture search with low-cost proxy metrics,” Information Sciences, vol. 655, p. 119856, 2024/01/01/2024, doi: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1016/j.ins.2023.119856" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1016/j.ins.2023.119856</a>">https://doi.org/10.1016/j.ins.2023.119856</ext-link>.
X. Ma, W. Tang, P. Wang, X. Guo, and L. Gao, “Extracting stage-specific and dynamic modules through analyzing multiple networks associated with cancer progression,” IEEE/ACM transactions on computational biology and bioinformatics, vol. 15, no. 2, pp. 647-658, 2016.
J. Huang et al., “Deep reinforcement learning-based trajectory pricing on ride-hailing platforms,” ACM Transactions on Intelligent Systems and Technology (TIST), vol. 13, no. 3, pp. 1-19, 2022.
Z. Song et al., “Robustness-aware 3d object detection in autonomous driving: A review and outlook,” IEEE Transactions on Intelligent Transportation Systems, 2024.
V. Uc-Cetina, N. Navarro-Guerrero, A. Martin-Gonzalez, C. Weber, and S. Wermter, “Survey on reinforcement learning for language processing,” Artificial Intelligence Review, vol. 56, no. 2, pp. 1543-1575, 2023.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778, 2016.
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700-4708, 2017.
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2818-2826, 2016.
B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le, “Learning transferable architectures for scalable image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 8697-8710, 2018.
Y. Chen et al., “Renas: Reinforced evolutionary neural architecture search,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 4787-4796, 2019.
Y. Shen et al., “Proxybo: Accelerating neural architecture search via bayesian optimization with zero-cost proxies,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, no. 8, pp. 9792-9801, 2023.
H. Jin, Q. Song, and X. Hu, “Auto-keras: An efficient neural architecture search system,” in Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp. 1946-1956, 2019.
Y. Li, R. Liu, X. Hao, R. Shang, P. Zhao, and L. Jiao, “EQNAS: Evolutionary Quantum Neural Architecture Search for Image Classification,” Neural Networks, vol. 168, pp. 471-483, 2023/11/01/2023, doi: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1016/j.neunet.2023.09.040" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1016/j.neunet.2023.09.040</a>">https://doi.org/10.1016/j.neunet.2023.09.040</ext-link>.
L. Xu, J. Zheng, C. He, J. Wang, B. Zheng, and J. Lv, “Adaptive Multi-particle Swarm Neural Architecture Search for High-incidence Cancer Prediction,” IEEE Transactions on Artificial Intelligence, 2025.
L. Wen, L. Gao, X. Li, and H. Li, “A new genetic algorithm based evolutionary neural architecture search for image classification,” Swarm and Evolutionary Computation, vol. 75, p. 101191, 2022.
V. Lopes, M. Santos, B. Degardin, and L. A. Alexandre, “Guided evolutionary neural architecture search with efficient performance estimation,” Neurocomputing, vol. 584, p. 127509, 2024/06/01/2024, doi: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1016/j.neucom.2024.127509" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1016/j.neucom.2024.127509</a>">https://doi.org/10.1016/j.neucom.2024.127509</ext-link>.
J. Liu, R. Cheng, and Y. Jin, “Bi-fidelity evolutionary multiobjective search for adversarially robust deep neural architectures,” Neurocomputing, vol. 550, p. 126465, 2023/09/14/2023, doi: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1016/j.neucom.2023.126465" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1016/j.neucom.2023.126465</a>">https://doi.org/10.1016/j.neucom.2023.126465</ext-link>.
S. Wang, Z. Liu, J. Li, M. Gong, and R. Yang, “Evolutionary Multitasking Collaborative Neural Architecture Search for Scene Classification,” in 2024 IEEE Congress on Evolutionary Computation (CEC), 30 June-5 July 2024 2024, pp. 1-8, doi: <a href="https://doi.org/10.1109/CEC60901.2024.10612042." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1109/CEC60901.2024.10612042.</a>
Y. Gao et al., “HGNAS++: Efficient Architecture Search for Heterogeneous Graph Neural Networks,” IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 9, pp. 9448-9461, 2023, doi: <a href="https://doi.org/10.1109/TKDE.2023.3239842." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1109/TKDE.2023.3239842.</a>
H. Wang, C. Ge, H. Chen, and X. Sun, “Pre-NAS: Preferred One-Shot Learning Towards Efficient Neural Architecture Search,” presented at the Proceedings of the 40th International Conference on Machine Learning, Proceedings of Machine Learning Research, [Online]. Available: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://proceedings.mlr.press/v202/wang23f.html">https://proceedings.mlr.press/v202/wang23f.html</ext-link>, 2023.
L. P. Kaelbling, M. L. Littman, and A. W. Moore, “Reinforcement learning: A survey,” Journal of artificial intelligence research, vol. 4, pp. 237-285, 1996.
B. Baker, O. Gupta, N. Naik, and R. Raskar, “Designing Neural Network Architectures using Reinforcement Learning,” in International Conference on Learning Representations, 2022.
B. Lyu, S. Wen, K. Shi, and T. Huang, “Multiobjective reinforcement learning-based neural architecture search for efficient portrait parsing,” IEEE Transactions on Cybernetics, vol. 53, no. 2, pp. 1158-1169, 2021.
R. S. Sukthanker, A. Krishnakumar, M. Safari, and F. Hutter, “Weight-entanglement meets gradient-based neural architecture search,” arXiv preprint arXiv:2312.10440, 2023.
Y. Wang et al., “MedNAS: Multiscale Training-Free Neural Architecture Search for Medical Image Analysis,” IEEE Transactions on Evolutionary Computation, vol. 28, no. 3, pp. 668-681, 2024, doi: <a href="https://doi.org/10.1109/TEVC.2024.3352641." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1109/TEVC.2024.3352641.</a>
Z. Fan, J. Wei, G. Zhu, J. Mo, and W. Li, “Evolutionary neural architecture search for retinal vessel segmentation,” arXiv preprint arXiv:2001.06678, 2020.
Y. Liu, Y. Sun, B. Xue, M. Zhang, G. G. Yen, and K. C. Tan, “A survey on evolutionary neural architecture search,” IEEE transactions on neural networks and learning systems, vol. 34, no. 2, pp. 550-570, 2021.
M. Suganuma, M. Kobayashi, S. Shirakawa, and T. Nagao, “Evolution of deep convolutional neural networks using cartesian genetic programming,” Evolutionary computation, vol. 28, no. 1, pp. 141-163, 2020.
S. Jiang, Z. Ji, G. Zhu, C. Yuan, and Y. Huang, “Operation-level early stopping for robustifying differentiable NAS,” Advances in Neural Information Processing Systems, vol. 36, pp. 70983-71007, 2023.
K. Sakamoto, H. Ishibashi, R. Sato, S. Shirakawa, Y. Akimoto, and H. Hino, “Atnas: Automatic termination for neural architecture search,” Neural Networks, vol. 166, pp. 446-458, 2023.
I. Trofimov, N. Klyuchnikov, M. Salnikov, A. Filippov, and E. Burnaev, “Multi-Fidelity Neural Architecture Search With Knowledge Distillation,” IEEE Access, vol. 11, pp. 59217-59225, 2023, doi: <a href="https://doi.org/10.1109/ACCESS.2023.3234810." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1109/ACCESS.2023.3234810.</a>
J. Liu, J. Yan, H. Xu, Z. Wang, J. Huang, and Y. Xu, “Finch: Enhancing Federated Learning With Hierarchical Neural Architecture Search,” IEEE Transactions on Mobile Computing, vol. 23, no. 5, pp. 6012-6026, 2024, doi: <a href="https://doi.org/10.1109/TMC.2023.3315451." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1109/TMC.2023.3315451.</a>
W. Wang, X. Zhang, H. Cui, H. Yin, and Y. Zhang, “FP-DARTS: Fast parallel differentiable neural architecture search for image classification,” Pattern Recognition, vol. 136, p. 109193, 2023/04/01/2023, doi: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1016/j.patcog.2022.109193" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1016/j.patcog.2022.109193</a>">https://doi.org/10.1016/j.patcog.2022.109193</ext-link>.
Z. Chen, G. Qiu, P. Li, L. Zhu, X. Yang, and B. Sheng, “MNGNAS: Distilling Adaptive Combination of Multiple Searched Networks for One-Shot Neural Architecture Search,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 11, pp. 13489-13508, 2023, doi: <a href="https://doi.org/10.1109/TPAMI.2023.3293885." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1109/TPAMI.2023.3293885.</a>
T. Zhang et al., “NASRec: Weight Sharing Neural Architecture Search for Recommender Systems,” presented at the Proceedings of the ACM Web Conference 2023, Austin, TX, USA, 2023. [Online]. Available: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1145/3543507.3583446" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1145/3543507.3583446</a>">https://doi.org/10.1145/3543507.3583446</ext-link>.
C. Peng, Y. Li, R. Shang, and L. Jiao, “RSBNet: One-shot neural architecture search for a backbone network in remote sensing image recognition,” Neurocomputing, vol. 537, pp. 110-127, 2023/06/07/2023, doi: <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="<a href="https://doi.org/10.1016/j.neucom.2023.03.046" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://doi.org/10.1016/j.neucom.2023.03.046</a>">https://doi.org/10.1016/j.neucom.2023.03.046</ext-link>.
X. Zhang, X. Zhou, M. Lin, and J. Sun, “Shufflenet: An extremely efficient convolutional neural network for mobile devices,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 6848-6856, 2018.
Z. Zhong, J. Yan, W. Wu, J. Shao, and C.-L. Liu, “Practical block-wise neural network architecture generation,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2423-2432, 2018.
H. Pham, M. Guan, B. Zoph, Q. Le, and J. Dean, “Efficient neural architecture search via parameters sharing,” in International conference on machine learning, PMLR, pp. 4095-4104, 2018.
H. Cai, T. Chen, W. Zhang, Y. Yu, and J. Wang, “Efficient architecture search by network transformation,” in Proceedings of the AAAI conference on artificial intelligence, vol. 32, no. 1, 2018.
J.-D. Dong, A.-C. Cheng, D.-C. Juan, W. Wei, and M. Sun, “Dpp-net: Device-aware progressive search for pareto-optimal neural architectures,” in Proceedings of the European conference on computer vision (ECCV), pp. 517-531, 2018.
B. Ma, J. Zhang, Y. Xia, and D. Tao, “VNAS: variational neural architecture search,” International Journal of Computer Vision, vol. 132, no. 9, pp. 3689-3713, 2024.
K. Jing, L. Chen, and J. Xu, “An architecture entropy regularizer for differentiable neural architecture search,” Neural Networks, vol. 158, pp. 111-120, 2023.
E. Real, A. Aggarwal, Y. Huang, and Q. V. Le, “Regularized evolution for image classifier architecture search,” in Proceedings of the aaai conference on artificial intelligence, vol. 33, no. 01, pp. 4780-4789, 2019.
H. Liu, K. Simonyan, O. Vinyals, C. Fernando, and K. Kavukcuoglu, “Hierarchical representations for efficient architecture search,” arXiv preprint arXiv:1711.00436, 2017.
Y. Sun, B. Xue, M. Zhang, G. G. Yen, and J. Lv, “Automatically designing CNN architectures using the genetic algorithm for image classification,” IEEE transactions on cybernetics, vol. 50, no. 9, pp. 3840-3854, 2020.
Z. Lu et al., “NSGA-Net: Neural architecture search using multi-objective genetic algorithm,” in Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 4750-4754, 2021.
Y. Sun, H. Wang, B. Xue, Y. Jin, G. G. Yen, and M. Zhang, “Surrogate-Assisted Evolutionary Deep Learning Using an End-to-End Random Forest-Based Performance Predictor,” IEEE Transactions on Evolutionary Computation, vol. 24, no. 2, 2020.
Y. Sun, B. Xue, M. Zhang, and G. G. Yen, “Completely Automated CNN Architecture Design Based on Blocks,” IEEE transactions on neural networks and learning systems, vol. 31, no. 4, pp. 1242-1254, 2020.
C. He, H. Tan, S. Huang, and R. Cheng, “Efficient evolutionary neural architecture search by modular inheritable crossover,” Swarm and Evolutionary Computation, vol. 64, p. 100894, 2021.
H. Zhang, Y. Jin, R. Cheng, and K. Hao, “Sampled training and node inheritance for fast evolutionary neural architecture search,” arXiv preprint arXiv:2003.11613, 2020.
Z. Yang et al., “Cars: Continuous evolution for efficient neural architecture search,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 1829-1838, 2020.
T. Elsken, J. H. Metzen, and F. Hutter, “Efficient multi-objective neural architecture search via lamarckian evolution,” arXiv preprint arXiv:1804.09081, 2018.
Y. Xue, X. Han, F. Neri, J. Qin, and D. Pelusi, “A gradient-guided evolutionary neural architecture search,” IEEE transactions on neural networks and learning systems, 2024.
Y. Xue, C. Chen, and A. Słowik, “Neural architecture search based on a multi-objective evolutionary algorithm with probability stack,” IEEE Transactions on Evolutionary Computation, vol. 27, no. 4, pp. 778-786, 2023.
Y. Tian, S. Peng, S. Yang, X. Zhang, K. C. Tan, and Y. Jin, “Action Command Encoding for Surrogate-Assisted Neural Architecture Search,” IEEE Transactions on Cognitive and Developmental Systems, vol. 14, no. 3, 2022.
X. Zheng et al., “Ddpnas: Efficient neural architecture search via dynamic distribution pruning,” International Journal of Computer Vision, vol. 131, no. 5, pp. 1234-1249, 2023.