Abstract
In this paper, we investigate two methods to enhance the efficiency of multi-objective evolutionary algorithms (MOEAs) when solving Neural Architecture Search (NAS) problems. The first method is to use a surrogate model to predict the accuracy of candidate architectures. Only promising architectures with high predicted accuracy values would then be truly trained and evaluated while the ones with low predicted accuracy would be discarded. The second method is to perform local search for potential solutions on the non-dominated front after each MOEA generation. To demonstrate the effectiveness of the proposed methods, we conduct experiments on benchmark datasets of both macro-level (MacroNAS) and micro-level (NAS-Bench-101, NAS-Bench-201) NAS problems. Experimental results exhibit that the proposed methods achieve improvements on the convergence speed of MOEAs toward Pareto-optimal fronts, especially for macro-level NAS problems.
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References
Bosman, P.A.N., Thierens, D.: The balance between proximity and diversity in multiobjective evolutionary algorithms. IEEE Trans. Evol. Comput. 7(2), 174–188 (2003)
Branke, J., Deb, K., Dierolf, H., Osswald, M.: Finding knees in multi-objective optimization. In: Yao, X., et al. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 722–731. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30217-9_73
Cai, H., Gan, C., Wang, T., Zhang, Z., Han, S.: Once-for-all: train one network and specialize it for efficient deployment. In: Proceedings of the International Conference on Learning Representations (ICLR) (2020)
Dai, X., et al.: ChamNet: towards efficient network design through platform-aware model adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)
Deb, K.: Multi-objective Optimization Using Evolutionary Algorithms. Wiley-Interscience Series in Systems and Optimization. Wiley, Hoboken (2001)
Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Dong, X., Yang, Y.: NAS-Bench-201: Extending the scope of reproducible neural architecture search. In: Proceedings of the International Conference on Learning Representations (ICLR) (2020)
Elsken, T., Metzen, J.H., Hutter, F.: Efficient multi-objective neural architecture search via Lamarckian evolution. In: Proceedings of the International Conference on Learning Representations (ICLR) (2019)
Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)
Kim, Y.H., Reddy, B., Yun, S., Seo, C.: NEMO: neuro-evolution with multiobjective optimization of deep neural network for speed and accuracy. In: ICML 2017 AutoML Workshop (2017)
Liu, C., et al.: Progressive neural architecture search. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11205, pp. 19–35. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01246-5_2
Lu, Z., Deb, K., Goodman, E., Banzhaf, W., Boddeti, V.N.: NSGANetV2: evolutionary multi-objective surrogate-assisted neural architecture search. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 35–51. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_3
Lu, Z., et al.: NSGA-Net: neural architecture search using multi-objective genetic algorithm. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) (2019)
Luong, H.N., Bosman, P.A.N.: Elitist archiving for multi-objective evolutionary algorithms: to adapt or not to adapt. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds.) PPSN 2012. LNCS, vol. 7492, pp. 72–81. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32964-7_8
Ottelander, T.D., Dushatskiy, A., Virgolin, M., Bosman, P.A.N.: Local search is a remarkably strong baseline for neural architecture search. In: Proceedings of the International Conference on Evolutionary Multi-Criterion Optimization (EMO) (2021)
Ying, C., Klein, A., Christiansen, E., Real, E., Murphy, K., Hutter, F.: Nas-bench-101: towards reproducible neural architecture search. In: International Conference on Machine Learning. pp. 7105–7114 (2019)
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This research is funded by University of Information Technology - Vietnam National University HoChiMinh City under grant number D1-2021-09.
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Phan, Q.M., Luong, N.H. (2021). Enhancing Multi-objective Evolutionary Neural Architecture Search with Surrogate Models and Potential Point-Guided Local Searches. In: Fujita, H., Selamat, A., Lin, J.CW., Ali, M. (eds) Advances and Trends in Artificial Intelligence. Artificial Intelligence Practices. IEA/AIE 2021. Lecture Notes in Computer Science(), vol 12798. Springer, Cham. https://doi.org/10.1007/978-3-030-79457-6_39
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