Uneingeschränkter Zugang

An Evolutionary Neural Architecture Search Method Accelerated by Multi-Fidelity Evaluation and Genetic Decision Controller

, ,  und   
11. Juli 2025

Zitieren
COVER HERUNTERLADEN

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: https://doi.org/10.1016/j.ins.2023.119856. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

Z. Song et al., “Robustness-aware 3d object detection in autonomous driving: A review and outlook,” IEEE Transactions on Intelligent Transportation Systems, 2024. Search in Google Scholar

C. Hema and F. P. G. Marquez, “Emotional speech recognition using cnn and deep learning techniques,” Applied Acoustics, vol. 211, p. 109492, 2023. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

B. Lyu et al., “Efficient multi-objective neural architecture search framework via policy gradient algorithm,” Information Sciences, vol. 661, p. 120186, 2024/03/01/2024, doi: https://doi.org/10.1016/j.ins.2024.120186. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

N. Sinha and K.-W. Chen, “Neural Architecture Search Using Covariance Matrix Adaptation Evolution Strategy,” Evolutionary Computation, vol. 32, no. 2, pp. 177-204, 2024, doi: 10.1162/evco a 00331. Search in Google Scholar

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: https://doi.org/10.1016/j.neunet.2023.09.040. Search in Google Scholar

S. Xiao, B. Zhao, and D. Liu, “Semi-supervised accuracy predictor-based multi-objective neural architecture search,” Neurocomputing, vol. 609, p. 128472, 2024/12/07/2024, doi: https://doi.org/10.1016/j.neucom.2024.128472. Search in Google Scholar

Q. M. Phan and N. H. Luong, “Enhancing multi-objective evolutionary neural architecture search with training-free Pareto local search,” Applied Intelligence, vol. 53, no. 8, pp. 8654-8672, 2023/04/01 2023, doi: 10.1007/s10489-022-04032-y. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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: https://doi.org/10.1016/j.neucom.2024.127509. Search in Google Scholar

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: https://doi.org/10.1016/j.neucom.2023.126465. Search in Google Scholar

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: 10.1109/CEC60901.2024.10612042. Search in Google Scholar

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: 10.1109/TKDE.2023.3239842. Search in Google Scholar

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: https://proceedings.mlr.press/v202/wang23f.html, 2023. Search in Google Scholar

L. Ma, H. Kang, G. Yu, Q. Li, and Q. He, “Single-Domain Generalized Predictor for Neural Architecture Search System,” IEEE Transactions on Computers, vol. 73, no. 5, pp. 1400-1413, 2024, doi: 10.1109/TC.2024.3365949. Search in Google Scholar

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. Search in Google Scholar

B. Baker, O. Gupta, N. Naik, and R. Raskar, “Designing Neural Network Architectures using Reinforcement Learning,” in International Conference on Learning Representations, 2022. Search in Google Scholar

R. Negrinho and G. Gordon, “Deeparchitect: Automatically designing and training deep architectures,” arXiv preprint arXiv:1704.08792, 2017. Search in Google Scholar

L. Kocsis and C. Szepesvári, “Bandit based monte-carlo planning,” in European conference on machine learning, Springer, pp. 282-293, 2006. Search in Google Scholar

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. Search in Google Scholar

S. Xie, H. Zheng, C. Liu, and L. Lin, “SNAS: stochastic neural architecture search,” arXiv preprint arXiv:1812.09926, 2018. Search in Google Scholar

H. Cai, L. Zhu, and S. Han, “Proxylessnas: Direct neural architecture search on target task and hardware,” arXiv preprint arXiv:1812.00332, 2018. Search in Google Scholar

Y. Xu et al., “Pc-darts: Partial channel connections for memory-efficient architecture search,” arXiv preprint arXiv:1907.05737, 2019. Search in Google Scholar

R. S. Sukthanker, A. Krishnakumar, M. Safari, and F. Hutter, “Weight-entanglement meets gradient-based neural architecture search,” arXiv preprint arXiv:2312.10440, 2023. Search in Google Scholar

L. Xie et al., “ZO-DARTS++: An Efficient and Size-Variable Zeroth-Order Neural Architecture Search Algorithm,” arXiv preprint arXiv:2503.06092, 2025. Search in Google Scholar

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: 10.1109/TEVC.2024.3352641. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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: 10.1109/ACCESS.2023.3234810. Search in Google Scholar

J. Sun, W. Yao, T. Jiang, and X. Chen, “Efficient search of comprehensively robust neural architectures via multi-fidelity evaluation,” Pattern Recognition, vol. 146, p. 110038, 2024/02/01/2024, doi: https://doi.org/10.1016/j.patcog.2023.110038. Search in Google Scholar

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: 10.1109/TMC.2023.3315451. Search in Google Scholar

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: https://doi.org/10.1016/j.patcog.2022.109193. Search in Google Scholar

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: 10.1109/TPAMI.2023.3293885. Search in Google Scholar

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: https://doi.org/10.1145/3543507.3583446. Search in Google Scholar

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: https://doi.org/10.1016/j.neucom.2023.03.046. Search in Google Scholar

A. Krizhevsky and G. Hinton, “Learning multiple layers of features from tiny images,” 2009. Search in Google Scholar

S. Zagoruyko, “Wide residual networks,” arXiv preprint arXiv:1605.07146, 2016. Search in Google Scholar

C. Szegedy et al., “Going deeper with convolutions,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1-9, 2015. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

H. Liu, K. Simonyan, and Y. Yang, “Darts: Differentiable architecture search,” arXiv preprint arXiv:1806.09055, 2018. Search in Google Scholar

R. Luo, F. Tian, T. Qin, E. Chen, and T.-Y. Liu, “Neural architecture optimization,” Advances in neural information processing systems, vol. 31, 2018. Search in Google Scholar

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. Search in Google Scholar

G. Biju and G. Pillai, “Sequential node search for faster neural architecture search,” Knowledge-Based Systems, vol. 300, p. 112145, 2024. Search in Google Scholar

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. Search in Google Scholar

K. Jing, L. Chen, and J. Xu, “An architecture entropy regularizer for differentiable neural architecture search,” Neural Networks, vol. 158, pp. 111-120, 2023. Search in Google Scholar

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. Search in Google Scholar

H. Liu, K. Simonyan, O. Vinyals, C. Fernando, and K. Kavukcuoglu, “Hierarchical representations for efficient architecture search,” arXiv preprint arXiv:1711.00436, 2017. Search in Google Scholar

E. Real et al., “Large-scale evolution of image classifiers,” in International conference on machine learning, PMLR, pp. 2902-2911, 2017. Search in Google Scholar

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. Search in Google Scholar

L. Xie and A. Yuille, “Genetic cnn,” in Proceedings of the IEEE international conference on computer vision, pp. 1379-1388, 2017. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

T. Elsken, J. H. Metzen, and F. Hutter, “Efficient multi-objective neural architecture search via lamarckian evolution,” arXiv preprint arXiv:1804.09081, 2018. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

Sprache:
Englisch
Zeitrahmen der Veröffentlichung:
4 Hefte pro Jahr
Fachgebiete der Zeitschrift:
Informatik, Datanbanken und Data Mining, Künstliche Intelligenz