{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:25:36Z","timestamp":1760711136711,"version":"3.37.3"},"reference-count":69,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2023,2,13]],"date-time":"2023-02-13T00:00:00Z","timestamp":1676246400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,2,13]],"date-time":"2023-02-13T00:00:00Z","timestamp":1676246400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62273357","61860206014"],"award-info":[{"award-number":["62273357","61860206014"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002767","name":"Hunan Provincial Science and Technology Department","doi-asserted-by":"publisher","award":["2019RS1003"],"award-info":[{"award-number":["2019RS1003"]}],"id":[{"id":"10.13039\/501100002767","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004735","name":"Natural Science Foundation of Hunan Province","doi-asserted-by":"publisher","award":["2021JJ20082"],"award-info":[{"award-number":["2021JJ20082"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2023,8]]},"DOI":"10.1007\/s13042-023-01794-w","type":"journal-article","created":{"date-parts":[[2023,2,13]],"date-time":"2023-02-13T21:41:12Z","timestamp":1676324472000},"page":"2723-2738","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A hierarchical evolution of neural architecture search method based on state transition algorithm"],"prefix":"10.1007","volume":"14","author":[{"given":"Yangyi","family":"Du","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaojun","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tingwen","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunhua","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,2,13]]},"reference":[{"key":"1794_CR1","doi-asserted-by":"crossref","unstructured":"Zhou X, Gao Y, Li C, Huang Z (2021) A multiple gradient descent design for multi-task learning on edge computing: multi-objective machine learning approach. IEEE Trans Netw Sci Eng 9(1):121-133","DOI":"10.1109\/TNSE.2021.3067454"},{"key":"1794_CR2","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","volume":"61","author":"J Schmidhuber","year":"2015","unstructured":"Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85\u2013117","journal-title":"Neural Netw"},{"key":"1794_CR3","unstructured":"Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. In: Advances in neural information processing systems, pp 3104\u20133112"},{"key":"1794_CR4","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der\u00a0Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"key":"1794_CR5","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"1794_CR6","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"key":"1794_CR7","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"1794_CR8","unstructured":"Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861"},{"key":"1794_CR9","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C (2018) MobileNetV2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510\u20134520","DOI":"10.1109\/CVPR.2018.00474"},{"key":"1794_CR10","doi-asserted-by":"crossref","unstructured":"Real E, Aggarwal A, Huang Y, Le QV (2019) Regularized evolution for image classifier architecture search. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 4780\u20134789","DOI":"10.1609\/aaai.v33i01.33014780"},{"issue":"1","key":"1794_CR11","first-page":"1997","volume":"20","author":"T Elsken","year":"2019","unstructured":"Elsken T, Metzen JH, Hutter F (2019) Neural architecture search: a survey. J Mach Learn Res 20(1):1997\u20132017","journal-title":"J Mach Learn Res"},{"key":"1794_CR12","unstructured":"Zoph B, Le QV (2016) Neural architecture search with reinforcement learning. arXiv preprint arXiv:1611.01578"},{"key":"1794_CR13","doi-asserted-by":"crossref","unstructured":"Guo J, Han K, Wang Y, Zhang C, Yang Z, Wu H, Chen X, Xu C (2020) Hit-detector: hierarchical trinity architecture search for object detection. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 11405\u201311414","DOI":"10.1109\/CVPR42600.2020.01142"},{"issue":"9","key":"1794_CR14","doi-asserted-by":"publisher","first-page":"6362","DOI":"10.1109\/TGRS.2020.2976694","volume":"58","author":"H Dong","year":"2020","unstructured":"Dong H, Zou B, Zhang L, Zhang S (2020) Automatic design of CNNS via differentiable neural architecture search for PolSAR image classification. IEEE Trans Geosci Remote Sens 58(9):6362\u20136375","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"1794_CR15","unstructured":"Liu Y, Sun Y, Xue B, Zhang M, Yen GG, Tan KC (2021) A survey on evolutionary neural architecture search. IEEE Trans Neural Netw Learn Syst"},{"key":"1794_CR16","doi-asserted-by":"crossref","unstructured":"Xie L, Yuille A (2017) Genetic CNN. In: Proceedings of the IEEE international conference on computer vision, pp 1379\u20131388","DOI":"10.1109\/ICCV.2017.154"},{"key":"1794_CR17","unstructured":"Real E, Moore S, Selle A, Saxena S, Suematsu YL, Tan J, Le QV, Kurakin A (2017) Large-scale evolution of image classifiers. In: International conference on machine learning, PMLR, pp 2902\u20132911"},{"key":"1794_CR18","doi-asserted-by":"crossref","unstructured":"Song D, Xu C, Jia X, Chen Y, Xu C, Wang Y (2020) Efficient residual dense block search for image super-resolution. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, pp 12007\u201312014","DOI":"10.1609\/aaai.v34i07.6877"},{"issue":"9","key":"1794_CR19","doi-asserted-by":"publisher","first-page":"3840","DOI":"10.1109\/TCYB.2020.2983860","volume":"50","author":"Y Sun","year":"2020","unstructured":"Sun Y, Xue B, Zhang M, Yen GG, Lv J (2020) Automatically designing CNN architectures using the genetic algorithm for image classification. IEEE Trans Cybern 50(9):3840\u20133854","journal-title":"IEEE Trans Cybern"},{"key":"1794_CR20","doi-asserted-by":"crossref","unstructured":"Zoph B, Vasudevan V, Shlens J, Le QV (2018) Learning transferable architectures for scalable image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8697\u20138710","DOI":"10.1109\/CVPR.2018.00907"},{"issue":"7","key":"1794_CR21","doi-asserted-by":"publisher","first-page":"2314","DOI":"10.1109\/TPAMI.2020.2969193","volume":"43","author":"Z Zhong","year":"2021","unstructured":"Zhong Z, Yang Z, Deng B, Yan J, Wu W, Shao J, Liu C (2021) BlockQNN: efficient block-wise neural network architecture generation. IEEE Trans Pattern Anal Mach Intell 43(7):2314\u20132328","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1794_CR22","unstructured":"Liu H, Simonyan K, Yang Y (2018) Darts: differentiable architecture search. arXiv preprint arXiv:1806.09055"},{"issue":"4","key":"1794_CR23","doi-asserted-by":"publisher","first-page":"1242","DOI":"10.1109\/TNNLS.2019.2919608","volume":"31","author":"Y Sun","year":"2019","unstructured":"Sun Y, Xue B, Zhang M, Yen GG (2019) Completely automated CNN architecture design based on blocks. IEEE Trans Neural Netw Learn Syst 31(4):1242\u20131254","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"1794_CR24","doi-asserted-by":"publisher","first-page":"864","DOI":"10.1016\/j.neucom.2015.08.041","volume":"173","author":"X Zhou","year":"2016","unstructured":"Zhou X, Gao DY, Yang C, Gui W (2016) Discrete state transition algorithm for unconstrained integer optimization problems. Neurocomputing 173:864\u2013874","journal-title":"Neurocomputing"},{"issue":"12","key":"1794_CR25","doi-asserted-by":"publisher","first-page":"1039","DOI":"10.3934\/jimo.2012.8.1039","volume":"33","author":"X Zhou","year":"2012","unstructured":"Zhou X, Yang C, Gui W (2012) State transition algorithm. J Ind Manag Optim 33(12):1039\u20131056","journal-title":"J Ind Manag Optim"},{"issue":"10","key":"1794_CR26","doi-asserted-by":"publisher","first-page":"3722","DOI":"10.1109\/TCYB.2018.2850350","volume":"49","author":"X Zhou","year":"2019","unstructured":"Zhou X, Yang C, Gui W (2019) A statistical study on parameter selection of operators in continuous state transition algorithm. IEEE Trans Cybern 49(10):3722\u20133730","journal-title":"IEEE Trans Cybern"},{"issue":"8","key":"1794_CR27","first-page":"1040","volume":"30","author":"C Yang","year":"2013","unstructured":"Yang C, Tang X, Zhou X, Gui W (2013) A discrete state transition algorithm for traveling salesman problem. Control Theory Appl 30(8):1040\u20131046","journal-title":"Control Theory Appl"},{"issue":"10","key":"1794_CR28","first-page":"1378","volume":"33","author":"T Dong","year":"2016","unstructured":"Dong T, Yang C, Zhou X, Gui W (2016) A novel discrete state transition algorithm for staff assignment problem. Control Theory Appl 33(10):1378\u20131388","journal-title":"Control Theory Appl"},{"issue":"4","key":"1794_CR29","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1080\/0305215X.2015.1025775","volume":"48","author":"X Zhou","year":"2016","unstructured":"Zhou X, Gao DY, Simpson AR (2016) Optimal design of water distribution networks by a discrete state transition algorithm. Eng Optim 48(4):603\u2013628","journal-title":"Eng Optim"},{"key":"1794_CR30","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.neucom.2019.01.009","volume":"334","author":"X Zhou","year":"2019","unstructured":"Zhou X, Yang K, Xie Y, Yang C, Huang T (2019) A novel modularity-based discrete state transition algorithm for community detection in networks. Neurocomputing 334:89\u201399","journal-title":"Neurocomputing"},{"issue":"5","key":"1794_CR31","doi-asserted-by":"publisher","first-page":"1888","DOI":"10.1109\/JBHI.2018.2872811","volume":"23","author":"Z Huang","year":"2019","unstructured":"Huang Z, Yang C, Zhou X, Huang T (2019) A hybrid feature selection method based on binary state transition algorithm and reliefF. IEEE J Biomed Health Inform 23(5):1888\u20131898","journal-title":"IEEE J Biomed Health Inform"},{"key":"1794_CR32","doi-asserted-by":"publisher","first-page":"106201","DOI":"10.1016\/j.mineng.2020.106201","volume":"153","author":"X Zhou","year":"2020","unstructured":"Zhou X, Zhang R, Yang C et al (2020) A hybrid feature selection method for production condition recognition in froth flotation with noisy labels. Miner Eng 153:106201","journal-title":"Miner Eng"},{"key":"1794_CR33","unstructured":"Cai H, Zhu L, Han S (2018) ProxylessNas: direct neural architecture search on target task and hardware. arXiv preprint arXiv:1812.00332"},{"key":"1794_CR34","unstructured":"Pham H, Guan M, Zoph B, Le Q, Dean J (2018) Efficient neural architecture search via parameters sharing. In: International conference on machine learning, PMLR, pp 4095\u20134104"},{"key":"1794_CR35","unstructured":"Xie S, Zheng H, Liu C, Lin L (2018) SNAS: stochastic neural architecture search. arXiv preprint arXiv:1812.09926"},{"key":"1794_CR36","doi-asserted-by":"crossref","unstructured":"Wei C, Niu C, Tang Y, Wang Y, Hu H, Liang J (2022) NPENAS: neural predictor guided evolution for neural architecture search. IEEE Trans Neural Netw Learn Syst","DOI":"10.1109\/TNNLS.2022.3151160"},{"issue":"2","key":"1794_CR37","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1109\/TEVC.2019.2924461","volume":"24","author":"Y Sun","year":"2019","unstructured":"Sun Y, Wang H, Xue B, Jin Y, Yen GG, Zhang M (2019) Surrogate-assisted evolutionary deep learning using an end-to-end random forest-based performance predictor. IEEE Trans Evol Comput 24(2):350\u2013364","journal-title":"IEEE Trans Evol Comput"},{"key":"1794_CR38","doi-asserted-by":"crossref","unstructured":"Tang Y, Wang Y, Xu Y, Chen H, Shi B, Xu C, Xu C, Tian Q, Xu C (2020) A semi-supervised assessor of neural architectures. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 1810\u20131819","DOI":"10.1109\/CVPR42600.2020.00188"},{"key":"1794_CR39","unstructured":"Guan C, Wang X, Zhu W (2021) AutoAttend: automated attention representation search. In: International conference on machine learning, PMLR, pp 3864\u20133874"},{"issue":"1","key":"1794_CR40","first-page":"100002","volume":"1","author":"S Liu","year":"2022","unstructured":"Liu S, Zhang H, Jin Y (2022) A survey on computationally efficient neural architecture search. J Autom Intell 1(1):100002","journal-title":"J Autom Intell"},{"key":"1794_CR41","doi-asserted-by":"crossref","unstructured":"Guo Z, Zhang X, Mu H, Heng W, Liu Z, Wei Y, Sun J (2020) Single path one-shot neural architecture search with uniform sampling. In: European conference on computer vision, Springer, pp 544\u2013560","DOI":"10.1007\/978-3-030-58517-4_32"},{"key":"1794_CR42","unstructured":"Bender G, Kindermans P-J, Zoph B, Vasudevan V, Le Q (2018) Understanding and simplifying one-shot architecture search. In: International conference on machine learning, PMLR, pp 550\u2013559"},{"key":"1794_CR43","unstructured":"Brock A, Lim T, Ritchie JM, Weston N (2017) SMASH: one-shot model architecture search through hypernetworks. arXiv preprint arXiv:1708.05344"},{"key":"1794_CR44","doi-asserted-by":"crossref","unstructured":"Wu B, Dai X, Zhang P, Wang Y, Sun F, Wu Y, Tian Y, Vajda P, Jia Y, Keutzer K (2019) FBNet: hardware-aware efficient ConvNET design via differentiable neural architecture search. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 10734\u201310742","DOI":"10.1109\/CVPR.2019.01099"},{"issue":"9","key":"1794_CR45","doi-asserted-by":"publisher","first-page":"2891","DOI":"10.1109\/TPAMI.2020.3020300","volume":"43","author":"X Zhang","year":"2020","unstructured":"Zhang X, Huang Z, Wang N, Xiang S, Pan C (2020) You only search once: single shot neural architecture search via direct sparse optimization. IEEE Trans Pattern Anal Mach Intell 43(9):2891\u20132904","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1794_CR46","unstructured":"Wang R, Cheng M, Chen X, Tang X, Hsieh C-J (2021) Rethinking architecture selection in differentiable NAS. In: International conference on learning representation"},{"key":"1794_CR47","first-page":"10503","volume":"33","author":"Y Yang","year":"2020","unstructured":"Yang Y, Li H, You S, Wang F, Qian C, Lin Z (2020) ISTA-NAS: efficient and consistent neural architecture search by sparse coding. Adv Neural Inf Process Syst 33:10503\u201310513","journal-title":"Adv Neural Inf Process Syst"},{"key":"1794_CR48","doi-asserted-by":"crossref","unstructured":"Veniat T, Denoyer L (2018) Learning time\/memory-efficient deep architectures with budgeted super networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3492\u20133500","DOI":"10.1109\/CVPR.2018.00368"},{"key":"1794_CR49","doi-asserted-by":"crossref","unstructured":"Wang Q, Wu B, Zhu P, Li P, Zuo W, Hu Q (2020) ECA-Net: efficient channel attention for deep convolutional neural networks. In: 2020 IEEE\/CVF conference on computer vision and pattern recognition, pp 11531\u201311539","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"1794_CR50","unstructured":"Ying C, Klein A, Christiansen E, Real E, Murphy K, Hutter F (2019) NAS-Bench-101: towards reproducible neural architecture search. In: International conference on machine learning, PMLR, pp 7105\u20137114"},{"key":"1794_CR51","doi-asserted-by":"crossref","unstructured":"Szegedy C, Ioffe S, Vanhoucke V, Alemi AA (2017) Inception-v4, inception-ResNet and the impact of residual connections on learning. In: Proceedings of the AAAI conference on artificial intelligence, pp 4278\u20134284","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"1794_CR52","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: IEEE conference on computer vision and pattern recognition, pp 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"key":"1794_CR53","doi-asserted-by":"crossref","unstructured":"Howard A, Pang R, Adam H, Le QV, Sandler M, Chen B, Wang W, Chen L, Tan M, Chu G, Vasudevan V, Zhu Y (2019) Searching for MobileNetV3. In: Proceedings of the IEEE international conference on computer vision, pp 1314\u20131324","DOI":"10.1109\/ICCV.2019.00140"},{"key":"1794_CR54","doi-asserted-by":"crossref","unstructured":"Yang Z, Wang Y, Chen X, Shi B, Xu C, Xu C, Tian Q, Xu C (2020) CARS: continuous evolution for efficient neural architecture search. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 1829\u20131838","DOI":"10.1109\/CVPR42600.2020.00190"},{"issue":"8","key":"1794_CR55","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780","journal-title":"Neural Comput"},{"issue":"10","key":"1794_CR56","doi-asserted-by":"publisher","first-page":"2451","DOI":"10.1162\/089976600300015015","volume":"12","author":"FA Gers","year":"2000","unstructured":"Gers FA, Schmidhuber J, Cummins F (2000) Learning to forget: continual prediction with LSTM. Neural Comput 12(10):2451\u20132471","journal-title":"Neural Comput"},{"issue":"2","key":"1794_CR57","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1287\/moor.13.2.311","volume":"13","author":"B Hajek","year":"1988","unstructured":"Hajek B (1988) Cooling schedules for optimal annealing. Math Oper Res 13(2):311\u2013329","journal-title":"Math Oper Res"},{"key":"1794_CR58","unstructured":"Krizhevsky A, Hinton G et al (2009) Learning multiple layers of features from tiny images. Master\u2019s thesis, University of Tront"},{"key":"1794_CR59","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) ImageNet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition, IEEE, pp 248\u2013255","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"1794_CR60","unstructured":"DeVries T, Taylor GW (2017) Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552"},{"key":"1794_CR61","unstructured":"Loshchilov I, Hutter F (2016) SGDR: stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983"},{"key":"1794_CR62","doi-asserted-by":"crossref","unstructured":"Liu C, Zoph B, Neumann M, Shlens J, Hua W, Li L-J, Fei-Fei L, Yuille A, Huang J, Murphy K (2018) Progressive neural architecture search. In: Proceedings of the European conference on computer vision, pp 19\u201334","DOI":"10.1007\/978-3-030-01246-5_2"},{"key":"1794_CR63","unstructured":"Baker B, Gupta O, Naik N, Raskar R (2016) Designing neural network architectures using reinforcement learning. arXiv preprint arXiv:1611.02167"},{"key":"1794_CR64","doi-asserted-by":"crossref","unstructured":"Lu Z, Whalen I, Boddeti V, Dhebar Y, Deb K, Goodman E, Banzhaf W (2019) NSGA-Net: neural architecture search using multi-objective genetic algorithm. In: Proceedings of the genetic and evolutionary computation conference, pp 419\u2013427","DOI":"10.1145\/3321707.3321729"},{"key":"1794_CR65","doi-asserted-by":"crossref","unstructured":"Elsken T, Metzen JH, Hutter F (2018) Efficient multi-objective neural architecture search via lamarckian evolution. arXiv preprint arXiv:1804.09081","DOI":"10.1007\/978-3-030-05318-5_3"},{"key":"1794_CR66","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"1794_CR67","doi-asserted-by":"crossref","unstructured":"Zhang X, Zhou X, Lin M, Sun J (2018) ShuffleNet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6848\u20136856","DOI":"10.1109\/CVPR.2018.00716"},{"key":"1794_CR68","doi-asserted-by":"crossref","unstructured":"Tan M, Chen B, Pang R, Vasudevan V, Sandler M, Howard A, Le QV (2019) MnasNet: platform-aware neural architecture search for mobile. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 2820\u20132828","DOI":"10.1109\/CVPR.2019.00293"},{"key":"1794_CR69","doi-asserted-by":"crossref","unstructured":"Zhou D, Zhou X, Zhang W, Loy CC, Yi S, Zhang X, Ouyang W (2020) EcoNAS: finding proxies for economical neural architecture search. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 11396\u201311404","DOI":"10.1109\/CVPR42600.2020.01141"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-01794-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-023-01794-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-023-01794-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,17]],"date-time":"2023-06-17T08:24:44Z","timestamp":1686990284000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-023-01794-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,13]]},"references-count":69,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["1794"],"URL":"https:\/\/doi.org\/10.1007\/s13042-023-01794-w","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2023,2,13]]},"assertion":[{"value":"22 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 January 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 February 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}