{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T02:27:30Z","timestamp":1775183250651,"version":"3.50.1"},"reference-count":167,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2022,6,10]],"date-time":"2022-06-10T00:00:00Z","timestamp":1654819200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,6,10]],"date-time":"2022-06-10T00:00:00Z","timestamp":1654819200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62066041"],"award-info":[{"award-number":["62066041"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41861047"],"award-info":[{"award-number":["41861047"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Northwest Normal University young teachers' scientific research capability upgrading program","award":["NWNU-LKQN-17-6"],"award-info":[{"award-number":["NWNU-LKQN-17-6"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2022,10]]},"DOI":"10.1007\/s13042-022-01578-8","type":"journal-article","created":{"date-parts":[[2022,6,10]],"date-time":"2022-06-10T15:04:04Z","timestamp":1654873444000},"page":"3001-3018","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Evolutionary neural networks for deep learning: a review"],"prefix":"10.1007","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5062-9224","authenticated-orcid":false,"given":"Yongjie","family":"Ma","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yirong","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,6,10]]},"reference":[{"issue":"5786","key":"1578_CR1","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1126\/science.1127647","volume":"313","author":"GE Hinton","year":"2006","unstructured":"Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504\u2013507","journal-title":"Science"},{"issue":"4","key":"1578_CR2","doi-asserted-by":"publisher","first-page":"1525","DOI":"10.1109\/TITS.2019.2910595","volume":"21","author":"F Yang","year":"2020","unstructured":"Yang F, Zhang L, Yu S et al (2020) Feature pyramid and hierarchical boosting network for pavement crack detection. IEEE Trans Intell Transp Syst 21(4):1525\u20131535","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"1578_CR3","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.neucom.2015.09.116","volume":"187","author":"Y Guo","year":"2016","unstructured":"Guo Y, Liu Y, Oerlemans A et al (2016) Deep learning for visual understanding: a review. Neurocomputing 187:27\u201348","journal-title":"Neurocomputing"},{"issue":"1","key":"1578_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3365211","volume":"38","author":"F Ahmad","year":"2020","unstructured":"Ahmad F, Abbasi A, Li J et al (2020) A deep learning architecture for psychometric natural language processing. ACM Trans Inf Syst (TOIS) 38(1):1\u201329","journal-title":"ACM Trans Inf Syst (TOIS)"},{"issue":"1","key":"1578_CR5","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1002\/mrm.27355","volume":"81","author":"F Knoll","year":"2019","unstructured":"Knoll F, Hammernik K, Yi Z (2019) Assessment of the generalization of learned image reconstruction and the potential for transfer learning. Magn Reson Med 81(1):116\u2013128","journal-title":"Magn Reson Med"},{"key":"1578_CR6","doi-asserted-by":"publisher","first-page":"5183","DOI":"10.1007\/s00521-020-05309-4","volume":"33","author":"A Mahindru","year":"2021","unstructured":"Mahindru A, Sangal AL (2021) MLDroid\u2014framework for Android malware detection using machine learning techniques. Neural Comput Appl 33:5183\u20135240","journal-title":"Neural Comput Appl"},{"key":"1578_CR7","doi-asserted-by":"publisher","unstructured":"Mahindru A, Sangal AL (2021) DeepDroid: feature selection approach to detect android malware using deep learning. In: Proceedings of the 2019 IEEE 10th International Conference on software engineering and service science (ICSESS). https:\/\/doi.org\/10.1109\/ICSESS47205.2019.9040821","DOI":"10.1109\/ICSESS47205.2019.9040821"},{"key":"1578_CR8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-40928-9_7","author":"A Mahindru","year":"2020","unstructured":"Mahindru A, Sangal AL (2020) PerbDroid: effective malware detection model developed using machine learning classification techniques. J Towards Bio-inspir Tech Softw Eng. https:\/\/doi.org\/10.1007\/978-3-030-40928-9_7","journal-title":"J Towards Bio-inspir Tech Softw Eng"},{"issue":"1","key":"1578_CR9","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1109\/TEVC.2018.2808689","volume":"23","author":"Y Sun","year":"2019","unstructured":"Sun Y, Yen GG et al (2019) Evolving unsupervised deep neural networks for learning meaningful representations. IEEE Trans Evol Comput 23(1):89\u2013103","journal-title":"IEEE Trans Evol Comput"},{"key":"1578_CR10","doi-asserted-by":"crossref","unstructured":"Liang J, Meyerson E, Hodjat B, Fink D, Mutch K, Miikkulainen R (2019) Evolutionary neural AutoML for deep learning. In: Proceedings of the genetic and evolutionary computation conference (GECCO), pp 401\u2013409","DOI":"10.1145\/3321707.3321721"},{"issue":"5","key":"1578_CR11","doi-asserted-by":"publisher","first-page":"672","DOI":"10.2174\/1574893610666151008012923","volume":"10","author":"E Fernandez-Blanco","year":"2015","unstructured":"Fernandez-Blanco E, Rivero D, Gestal M et al (2015) A Hybrid evolutionary system for automated artificial neural networks generation and simplification in biomedical applications. Curr Bioinform 10(5):672\u2013691","journal-title":"Curr Bioinform"},{"key":"1578_CR12","doi-asserted-by":"crossref","unstructured":"Lehman J, Chen J, Clune J, Stanley KO (2018) Safe mutations for deep and recurrent neural networks through output gradients. In: Proceedings of the genetic and evolutionary computation conference (GECCO), pp 117\u2013124","DOI":"10.1145\/3205455.3205473"},{"key":"1578_CR13","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1007\/978-3-642-41888-4_5","volume-title":"Recent advances in the theory and application of fitness landscapes","author":"G Lu","year":"2014","unstructured":"Lu G, Li J, Yao X (2014) Fitness landscapes and problem difficulty in evolutionary algorithms: from theory to applications. In: Richter H (ed) Recent advances in the theory and application of fitness landscapes. Springer Berlin Heidelberg, Berlin, pp 133\u2013152"},{"issue":"14","key":"1578_CR14","doi-asserted-by":"publisher","first-page":"817","DOI":"10.1016\/S0950-5849(01)00188-4","volume":"43","author":"D Whitley","year":"2001","unstructured":"Whitley D (2001) An overview of evolutionary algorithms: practical issues and common pitfalls. Inf Softw Technol 43(14):817\u2013831","journal-title":"Inf Softw Technol"},{"issue":"1","key":"1578_CR15","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1038\/s42256-018-0006-z","volume":"1","author":"KO Stanley","year":"2019","unstructured":"Stanley KO, Clune J, Lehman J et al (2019) Designing neural networks through neuroevolution. Nat Mach Intell 1(1):24\u201335","journal-title":"Nat Mach Intell"},{"issue":"11","key":"1578_CR16","doi-asserted-by":"publisher","first-page":"1703","DOI":"10.1101\/gr.192502","volume":"12","author":"M Alvaro","year":"2002","unstructured":"Alvaro M, Joaquin D, Ronald J et al (2002) Systematic learning of gene functional classes from DNA array expression data by using multilayer perceptrons. Genome Res 12(11):1703\u20131715","journal-title":"Genome Res"},{"issue":"6088","key":"1578_CR17","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1038\/323533a0","volume":"323","author":"DE Rumelhart","year":"1986","unstructured":"Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323(6088):533\u2013536","journal-title":"Nature"},{"issue":"6","key":"1578_CR18","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks. Commun ACM 60(6):84\u201390","journal-title":"Commun ACM"},{"key":"1578_CR19","unstructured":"Zeiler MD, Fergus R (2013) Visualizing and understanding convolutional networks. arXiv preprint, arXiv:1311.2901"},{"key":"1578_CR20","unstructured":"Simonyan K, Zisserman A (2014) very deep convolutional networks for large-scale image recognition. arXiv preprint, arXiv 1409.1556"},{"key":"1578_CR21","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 (CVPR)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"1578_CR22","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 (CVPR)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"7587","key":"1578_CR23","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1038\/nature16961","volume":"529","author":"D Silver","year":"2016","unstructured":"Silver D, Huang A, Maddison CJ et al (2016) Mastering the game of Go with deep neural networks and tree search. Nature 529(7587):484\u2013489","journal-title":"Nature"},{"key":"1578_CR24","doi-asserted-by":"crossref","unstructured":"He KM, Sun J (2015) Convolutional neural networks at constrained time cost. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2015.7299173"},{"issue":"7553","key":"1578_CR25","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y Lecun","year":"2015","unstructured":"Lecun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436","journal-title":"Nature"},{"issue":"3","key":"1578_CR26","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1007\/s10462-011-9270-6","volume":"39","author":"S Ding","year":"2013","unstructured":"Ding S, Li H, Su C et al (2013) Evolutionary artificial neural networks: a review. Artif Intell Rev 39(3):251\u2013260","journal-title":"Artif Intell Rev"},{"issue":"3","key":"1578_CR27","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1109\/TEVC.2005.846356","volume":"9","author":"Y Jin","year":"2005","unstructured":"Jin Y, J\u00fcrgen B (2005) Evolutionary optimization in uncertain environments-a survey. IEEE Trans Evol Comput 9(3):303\u2013317","journal-title":"IEEE Trans Evol Comput"},{"issue":"1","key":"1578_CR28","first-page":"40","volume":"45","author":"N Sharkey","year":"2002","unstructured":"Sharkey N (2002) Evolutionary computation: the fossil record. IEE Rev 45(1):40\u201340","journal-title":"IEE Rev"},{"issue":"2","key":"1578_CR29","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1109\/TEVC.2019.2916183","volume":"24","author":"Y Sun","year":"2017","unstructured":"Sun Y, Xue B, Zhang M (2017) Evolving deep convolutional neural networks for image classification. IEEE Trans Evol Comput 24(2):394\u2013407","journal-title":"IEEE Trans Evol Comput"},{"issue":"3","key":"1578_CR30","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1109\/TEVC.2005.857695","volume":"10","author":"PP Bonissone","year":"2006","unstructured":"Bonissone PP, Subbu R, Eklund N et al (2006) Evolutionary algorithms + domain knowledge = real-world evolutionary computation. IEEE Trans Evol Comput 10(3):256\u2013280","journal-title":"IEEE Trans Evol Comput"},{"key":"1578_CR31","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/j.engappai.2017.01.013","volume":"60","author":"VK Ojha","year":"2017","unstructured":"Ojha VK, Abraham A, Snasel V (2017) Metaheuristic design of feedforward neural networks: a review of two decades of research. Eng Appl Artif Intell 60:97\u2013116","journal-title":"Eng Appl Artif Intell"},{"issue":"2","key":"1578_CR32","first-page":"95","volume":"3","author":"JJ Grefenstette","year":"1988","unstructured":"Grefenstette JJ (1988) Genetic algorithms and machine learning. Mach Learn 3(2):95\u201399","journal-title":"Mach Learn"},{"key":"1578_CR33","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1007\/3-540-32494-1_4","volume":"192","author":"N Hansen","year":"2006","unstructured":"Hansen N (2006) The CMA evolution strategy: a comparing review. Stud Fuzziness Soft Comput 192:75\u2013102","journal-title":"Stud Fuzziness Soft Comput"},{"issue":"3","key":"1578_CR34","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1109\/5254.846288","volume":"15","author":"W Banzhaf","year":"2000","unstructured":"Banzhaf W, Koza JR (2000) Genetic programming. IEEE Intell Syst 15(3):74\u201384","journal-title":"IEEE Intell Syst"},{"issue":"6","key":"1578_CR35","doi-asserted-by":"publisher","first-page":"30977","DOI":"10.4249\/scholarpedia.30977","volume":"8","author":"J Lehman","year":"2013","unstructured":"Lehman J, Miikkulainen R (2013) Neuroevolution. Scholarpedia 8(6):30977","journal-title":"Scholarpedia"},{"key":"1578_CR36","doi-asserted-by":"crossref","unstructured":"Dufourq E, Bassett B (2017) Automated problem identification: regression vs classification via evolutionary deep networks. In: Proceedings of the South African institute of computer scientists and information technologists","DOI":"10.1145\/3129416.3129429"},{"issue":"4","key":"1578_CR37","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1007\/s00158-010-0575-x","volume":"43","author":"D Sharma","year":"2011","unstructured":"Sharma D, Deb K et al (2011) Domain-specific initial population strategy for compliant mechanisms using customized genetic algorithm. Struct Multidiscip Optim 43(4):541\u2013554","journal-title":"Struct Multidiscip Optim"},{"issue":"1","key":"1578_CR38","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1007\/s00158-006-0007-0","volume":"32","author":"JA Madeira","year":"2006","unstructured":"Madeira JA, Rodrigues HC et al (2006) Multiobjective topology optimization of structures using genetic algorithms with chromosome repairing. Struct Multidiscip Optim 32(1):31\u201339","journal-title":"Struct Multidiscip Optim"},{"key":"1578_CR39","doi-asserted-by":"crossref","unstructured":"Lorenzo PR, Nalepa J (2018) Memetic evolution of deep neural networks. In: Proceedings of the genetic and evolutionary computation conference (GECCO)","DOI":"10.1145\/3205455.3205631"},{"key":"1578_CR40","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3100554","author":"Y Liu","year":"2021","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. https:\/\/doi.org\/10.1109\/TNNLS.2021.3100554","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"1578_CR41","doi-asserted-by":"crossref","unstructured":"Sapra D, Pimentel AD (2020) An evolutionary optimization algorithm for gradually saturating objective functions. In: Proceedings of GECCO","DOI":"10.1145\/3377930.3389834"},{"key":"1578_CR42","unstructured":"Montana D, Davis L et al (1989) Training feedforward neural networks using genetic algorithms. In: Proceedings of the international joint conference on Artificial intelligence (IJCAI). vol 1, pp 762\u2013767"},{"key":"1578_CR43","doi-asserted-by":"crossref","unstructured":"Yao X (1999) Evolving artificial neural networks. In: Proceedings of the IEEE, 87(9):1423\u20131447","DOI":"10.1109\/5.784219"},{"key":"1578_CR44","doi-asserted-by":"crossref","unstructured":"Lehman J, Chen J, Clune J et al (2018) ES is more than just a traditional finite-difference approximator. In: Proceedings of the genetic and evolutionary computation conference (GECCO)","DOI":"10.1145\/3205455.3205474"},{"key":"1578_CR45","unstructured":"Salimans T, Ho J, Chen X, et al. (2017) evolution strategies as a scalable alternative to reinforcement learning. arXiv preprint, arXiv:1703.03864"},{"key":"1578_CR46","unstructured":"Such F P, Madhavan V, Conti E, et al. (2017) Deep neuroevolution: genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning. arXiv preprint, arXiv: 1712.06567"},{"key":"1578_CR47","doi-asserted-by":"publisher","first-page":"6863","DOI":"10.1007\/s00521-018-3518-x","volume":"31","author":"GAP Singh","year":"2018","unstructured":"Singh GAP, Gupta PK (2018) Performance analysis of various machine learning-based approaches for detection and classification of lung cancer in humans. Neural Comput Appl 31:6863\u20136877","journal-title":"Neural Comput Appl"},{"key":"1578_CR48","unstructured":"Cui X, Zhang W, T\u00fcske Z, Picheny M (2018) Evolutionary stochastic gradient descent for optimization of deep neural networks. In: Proceedings of the 32nd international conference on neural information processing systems (NIPS)"},{"key":"1578_CR49","unstructured":"Khadka S, Tumer K (2018) Evolution-guided policy gradient in reinforcement learning. In: In: Proceedings of the 32nd international conference on neural information processing systems (NIPS)"},{"key":"1578_CR50","unstructured":"Houthooft R, Chen Y, Isola P, Stadie B, Wolski F, Jonathan H, OpenAI, Abbeel P (2018) Evolved policy gradients. In: Proceedings of the 32nd international conference on neural information processing systems (NIPS)"},{"key":"1578_CR51","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3061630","author":"S Yang","year":"2021","unstructured":"Yang S, Tian Y, He C, Zhang X, Tan KC, Jin Y (2021) A Gradient-guided evolutionary approach to training deep neural networks. IEEE Trans Neural Netw Learn Syst. https:\/\/doi.org\/10.1109\/TNNLS.2021.3061630","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"1578_CR52","unstructured":"Liu H, Simonyan K, Vinyals O, et al (2018) Genetic programming approach to designing convolutional Architecture Search. In: Proceedings of the ICLR"},{"key":"1578_CR53","unstructured":"Hu H, Peng R, Tai YW, et al (2016) Network trimming: a data-driven neuron pruning approach towards efficient deep architectures. arXiv preprint, arXiv:1607.03250"},{"issue":"99","key":"1578_CR54","doi-asserted-by":"publisher","first-page":"2450","DOI":"10.1109\/TNNLS.2017.2695223","volume":"29","author":"J Liu","year":"2018","unstructured":"Liu J, Gong M, Miao Q et al (2018) Structure learning for deep neural networks based on multiobjective optimization. IEEE Trans Neural Netw Learn Syst 29(99):2450\u20132463","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"1578_CR55","unstructured":"Kim YH, Reddy B, Yun S, Seo C (2017) Nemo: neuro-evolution with multiobjective optimization of deep neural network for speed and accuracy. In: Proceedings of the international conference on machine learning (ICML)"},{"key":"1578_CR56","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.neucom.2020.04.079","volume":"406","author":"Y Zhou","year":"2020","unstructured":"Zhou Y, Jin Y, Ding J (2020) Surrogate-assisted evolutionary search of spiking neural architectures in liquid state machines. Neurocomputing 406:12\u201323","journal-title":"Neurocomputing"},{"key":"1578_CR57","unstructured":"Probst P, Bischl B, Boulesteix AL (2018) Tunability: Importance of hyperparameters of machine learning algorithms. arXiv preprint, arXiv:1802.09596"},{"issue":"1","key":"1578_CR58","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1515\/comp-2019-0011","volume":"9","author":"R Ghawi","year":"2019","unstructured":"Ghawi R, Pfeffer J (2019) Efficient hyperparameter tuning with grid search for text categorization using kNN approach with BM25 similarity. Open Comput Sci 9(1):160\u2013180","journal-title":"Open Comput Sci"},{"issue":"1","key":"1578_CR59","first-page":"281","volume":"13","author":"J Bergstra","year":"2012","unstructured":"Bergstra J, Bengio Y (2012) Random search for hyper-parameter optimization. J Mach Learn Res 13(1):281\u2013305","journal-title":"J Mach Learn Res"},{"key":"1578_CR60","unstructured":"Loshchilov I, Hutter F (2016) CMA-ES for hyperparameter optimization of deep neural networks. arXiv preprint, arXiv:1604.07269"},{"key":"1578_CR61","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1016\/j.neucom.2019.11.090","volume":"381","author":"R ZahediNasaba","year":"2020","unstructured":"ZahediNasaba R, Mohsenia H (2020) Neuroevolutionary based convolutional neural network with adaptive activation functions. Neurocomputing 381:306\u2013313","journal-title":"Neurocomputing"},{"issue":"2","key":"1578_CR62","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1162\/106365602320169811","volume":"10","author":"KO Stanley","year":"2002","unstructured":"Stanley KO, Miikkulainen R (2002) Evolving neural networks through augmenting topologies. Evol Comput 10(2):99\u2013127","journal-title":"Evol Comput"},{"key":"1578_CR63","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/S0925-2312(03)00369-2","volume":"56","author":"A Abraham","year":"2004","unstructured":"Abraham A (2004) Meta learning evolutionary artificial neural networks. Neurocomputing 56:1\u201338","journal-title":"Neurocomputing"},{"key":"1578_CR64","unstructured":"Miikkulainen R, Liang J, Meyerson E, et al (2017) Evolving deep neural networks. arXiv preprint, arXiv:1703.00548"},{"key":"1578_CR65","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/s10710-018-9339-y","volume":"20","author":"F Assun\u00e7\u00e3o","year":"2018","unstructured":"Assun\u00e7\u00e3o F, Louren\u00e7o N, Machado P et al (2018) DENSER: deep evolutionary network structured representation. Genet Program Evol Mach 20:5\u201335","journal-title":"Genet Program Evol Mach"},{"key":"1578_CR66","first-page":"197","volume":"11451","author":"F Assuno","year":"2019","unstructured":"Assuno F, Loureno N, Machado P et al (2019) Fast DENSER: efficient deep neuroevolution. Genetic Programming 11451:197\u2013212","journal-title":"Genetic Programming"},{"key":"1578_CR67","unstructured":"Minar M R, Naher J (2018) Recent advances in deep learning: an overview. arXiv preprint, arXiv:1807.08169"},{"key":"1578_CR68","doi-asserted-by":"crossref","unstructured":"Suganuma M, Shirakawa S, Nagao T (2017) A genetic programming approach to designing convolutional neural network architectures. In: Proceedings of the genetic and evolutionary computation conference, pp 497\u2013504","DOI":"10.1145\/3071178.3071229"},{"key":"1578_CR69","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1016\/j.neucom.2019.10.007","volume":"379","author":"B Ma","year":"2020","unstructured":"Ma B, Li X, Xia Y et al (2020) Autonomous deep learning: a genetic DCNN designer for image classification. Neurocomputing 379:152\u2013161","journal-title":"Neurocomputing"},{"key":"1578_CR70","doi-asserted-by":"crossref","unstructured":"Xie L, Yuille A (2017) Genetic CNN. In: Proceedings of the IEEE international conference on computer vision (ICCV), pp 1379\u20131388","DOI":"10.1109\/ICCV.2017.154"},{"issue":"1","key":"1578_CR71","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1162\/evco_a_00253","volume":"28","author":"M Suganuma","year":"2020","unstructured":"Suganuma M, Kobayashi M, Shirakawa S et al (2020) Evolution of deep convolutional neural networks using Cartesian genetic programming. Evol Comput 28(1):141\u2013163","journal-title":"Evol Comput"},{"issue":"4","key":"1578_CR72","first-page":"1","volume":"31","author":"Y Sun","year":"2019","unstructured":"Sun Y, Xue B, Zhang M et al (2019) Completely automated CNN architecture design based on blocks. IEEE Trans Neural Netw Learn Syst 31(4):1\u201313","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"1578_CR73","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: Proceedings of the international conference on machine learning (ICML)"},{"key":"1578_CR74","unstructured":"Zhang H, Kiranyaz S, Gabbouj M (2018) Finding better topologies for deep convolutional neural networks by evolution. arXiv preprint, arXiv:1809.03242"},{"key":"1578_CR75","doi-asserted-by":"crossref","unstructured":"Desell T (2017) Large scale evolution of convolutional neural networks using volunteer computing. In: Proceedings of the the genetic and evolutionary computation conference companion (GECCO)","DOI":"10.1145\/3067695.3076002"},{"key":"1578_CR76","doi-asserted-by":"crossref","unstructured":"ElSaid A, Wild B, Higgins J, Desell T (2016) Using LSTM recurrent neural networks to predict excess vibration events in aircraft engines. In: Proceedings of 2016 IEEE 12th international conference on e-Science (e-Science), pp 260\u2013269","DOI":"10.1109\/eScience.2016.7870907"},{"key":"1578_CR77","doi-asserted-by":"crossref","unstructured":"Ororbia A, ElSaid A E, Desell T (2019) Investigating recurrent neural network memory structures using neuro-evolution. In: Proceedings of the genetic and evolutionary computation conference (GECCO)","DOI":"10.1145\/3321707.3321795"},{"key":"1578_CR78","doi-asserted-by":"crossref","unstructured":"Real E, Aggarwal A, Huang Y, Le QV (2018) Regularized evolution for image classifier architecture search. In: Proceedings of the AAAI conference on artificial intelligence. vol 33(01), pp 4780\u20134789","DOI":"10.1609\/aaai.v33i01.33014780"},{"key":"1578_CR79","unstructured":"Rawal A, Miikkulainen R (2018) From nodes to networks: evolving recurrent neural networks. arXiv preprint, arXiv:1803.04439"},{"key":"1578_CR80","unstructured":"Zoph B, Le Q V (2016) Neural architecture search with reinforcement learning. arXiv preprint, arXiv:1611.01578"},{"key":"1578_CR81","unstructured":"Pham H, Guan M, Zoph B, Le Q, Dean J (2018) Efficient neural architecture search via parameters sharing. In: Proceedings of the 35th International conference on machine learning, PMLR 80, pp 4095\u20134104"},{"issue":"7","key":"1578_CR82","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":"1578_CR83","unstructured":"Baker B, Gupta O, Naik N, Raskar R (2016) Designing neural network architectures using reinforcement learning. arXiv preprint arXiv:1611.02167"},{"key":"1578_CR84","doi-asserted-by":"crossref","unstructured":"Dong J, Cheng A, Juan D, Wei W, Sun M (2018) Dpp-net: device-aware progressive search for pareto-optimal neural architectures. In: Proceedings of the 6th international conference on learning representations (ICLR). https:\/\/openreview.net\/forum?id=B1NT3TAIM","DOI":"10.1007\/978-3-030-01252-6_32"},{"key":"1578_CR85","doi-asserted-by":"crossref","unstructured":"Dong J, Cheng A, Juan D, Wei W, Sun M (2018) Ppp-net: platform-aware progressive search for pareto-optimal neural architectures. In: Proceedings of 6th international conference on learning representations (ICLR)","DOI":"10.1007\/978-3-030-01252-6_32"},{"key":"1578_CR86","unstructured":"Cai H, Zhu L, Han S (2018) Proxylessnas: direct neural architecture search on target task and hardware. arXiv preprint arXiv:1812.00332"},{"key":"1578_CR87","unstructured":"Liu H, Simonyan K, Yang Y (2018) Darts: differentiable architecture search. arXiv preprint arXiv:1806.09055"},{"key":"1578_CR88","unstructured":"Jin X, Wang J, Slocum J, Yang M, Dai S, Yan S, Feng J (2019) Rc-darts: resource constrained differentiable architecture search. arXiv preprint arXiv:1912.12814"},{"key":"1578_CR89","unstructured":"Xie S, Zheng H, Liu C, Lin L (2018) Snas: stochastic neural architecture search. arXiv preprint arXiv:1812.09926"},{"issue":"9","key":"1578_CR90","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"},{"issue":"2","key":"1578_CR91","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"},{"issue":"2","key":"1578_CR92","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1109\/TEVC.2020.3040272","volume":"25","author":"H Zhang","year":"2020","unstructured":"Zhang H, Jin Y, Cheng R, Hao K (2020) Efficient evolutionary search of attention convolutional networks via sampled training and node inheritance. IEEE Trans Evol Comput 25(2):371\u2013385","journal-title":"IEEE Trans Evol Comput"},{"key":"1578_CR93","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":"1578_CR94","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2022.3143657","author":"F Ming","year":"2022","unstructured":"Ming F, Gong W, Wang L (2022) A two-stage evolutionary algorithm with balanced convergence and diversity for many-objective optimization. IEEE Trans Syst Man Cybern Syst. https:\/\/doi.org\/10.1109\/TSMC.2022.3143657","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"issue":"5","key":"1578_CR95","doi-asserted-by":"publisher","first-page":"748","DOI":"10.1109\/TEVC.2018.2882166","volume":"23","author":"Y Sun","year":"2019","unstructured":"Sun Y, Xue B, Zhang M, Yen GG (2019) A new two-stage evolutionary algorithm for many-objective optimization. IEEE Trans Evol Comput 23(5):748\u2013761","journal-title":"IEEE Trans Evol Comput"},{"key":"1578_CR96","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1016\/j.neucom.2019.10.053","volume":"378","author":"J Huang","year":"2020","unstructured":"Huang J, Sun W, Huang L (2020) Deep neural networks compression learning based on multi-objective evolutionary algorithms. Neurocomputing 378:260\u2013269","journal-title":"Neurocomputing"},{"key":"1578_CR97","doi-asserted-by":"publisher","DOI":"10.1016\/j.micpro.2020.102989","volume":"73","author":"M Loni","year":"2020","unstructured":"Loni M, Sinaei S, Zoljodi A, Daneshtalab M, Sj\u00f6din M (2020) DeepMaker: a multi-objective optimization framework for deep neural networks in embedded systems. Microprocess Microsyst 73:102989","journal-title":"Microprocess Microsyst"},{"key":"1578_CR98","doi-asserted-by":"crossref","unstructured":"Cetto T, Byrne J, Xu X et al (2019) Size\/accuracy trade-off in convolutional neural networks: an evolutionary approach. In: Proceedings of the INNSBDDL","DOI":"10.1007\/978-3-030-16841-4_3"},{"key":"1578_CR99","doi-asserted-by":"crossref","unstructured":"Nolfi S, Miglino O, Parisi D (1994) Phenotypic plasticity in evolving neural networks. In: Proceedings of the PerAc'94. From perception to action, pp 146\u2013157","DOI":"10.1109\/FPA.1994.636092"},{"key":"1578_CR100","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.neunet.2018.07.013","volume":"108","author":"A Soltoggio","year":"2018","unstructured":"Soltoggio A, Stanley KO, Risi S (2018) Born to learn: the inspiration, progress, and future of evolved plastic artificial neural networks. Neural Netw 108:48\u201367","journal-title":"Neural Netw"},{"key":"1578_CR101","doi-asserted-by":"crossref","unstructured":"Chalmers DJ (1991) The evolution of learning: An experiment in genetic connectionism. In: Connectionist models. Morgan Kaufmann, Elsevier, pp 81\u201390","DOI":"10.1016\/B978-1-4832-1448-1.50014-7"},{"issue":"1","key":"1578_CR102","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/0925-2312(96)00009-4","volume":"11","author":"HB Kim","year":"1996","unstructured":"Kim HB, Jung SH, Kim TG et al (1996) Fast learning method for back-propagation neural network by evolutionary adaptation of learning rates. Neurocomputing 11(1):101\u2013106","journal-title":"Neurocomputing"},{"issue":"1","key":"1578_CR103","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1177\/1059-712302-010001-01","volume":"10","author":"Y Niv","year":"2002","unstructured":"Niv Y, Joel D, Meilijson I et al (2002) Evolution of reinforcement learning in uncertain environments: a simple explanation for complex foraging behaviors. Adapt Behav 10(1):5\u201324","journal-title":"Adapt Behav"},{"key":"1578_CR104","volume-title":"The organization of behavior: a neuropsychological theory","author":"DO Hebb","year":"1949","unstructured":"Hebb DO (1949) The organization of behavior: a neuropsychological theory. Wiley, New York"},{"issue":"1","key":"1578_CR105","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1523\/JNEUROSCI.02-01-00032.1982","volume":"2","author":"EL Bienenstock","year":"1982","unstructured":"Bienenstock EL, Cooper LN, Munro PW (1982) Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex. J Neurosci 2(1):32\u201348","journal-title":"J Neurosci"},{"key":"1578_CR106","doi-asserted-by":"crossref","unstructured":"Babinec \u0160, Posp\u00edchal J (2007) Improving the prediction accuracy of echo state neural networks by anti-Oja\u2019s learning. In: Proceedings of the International Conference on artificial neural networks, Springer, pp 19\u201328","DOI":"10.1007\/978-3-540-74690-4_3"},{"issue":"24","key":"1578_CR107","doi-asserted-by":"publisher","first-page":"10464","DOI":"10.1523\/JNEUROSCI.18-24-10464.1998","volume":"18","author":"GQ Bi","year":"1998","unstructured":"Bi GQ, Poo MM (1998) Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. J Neurosci 18(24):10464\u201310472","journal-title":"J Neurosci"},{"key":"1578_CR108","doi-asserted-by":"crossref","unstructured":"Triesch J (2005) A gradient rule for the plasticity of a neuron\u2019s intrinsic excitability. In: Proceedings of the artificial neural networks: biological inspirations(ICANN). vol 3696, Springer, pp 65\u201370","DOI":"10.1007\/11550822_11"},{"key":"1578_CR109","doi-asserted-by":"crossref","unstructured":"Coleman OJ, Blair AD (2012) Evolving plastic neural networks for online learning: review and future directions. In: Proceedings of the Australasian joint conference on artificial intelligence, pp 326\u2013337","DOI":"10.1007\/978-3-642-35101-3_28"},{"key":"1578_CR110","unstructured":"Stanley KO (2017) Neuroevolution: a different kind of deep learning. Obtenido de. 27(04):2019"},{"key":"1578_CR111","doi-asserted-by":"crossref","unstructured":"Risi S, Stanley KO (2014) Guided self-organization in indirectly encoded and evolving topographic maps. In: Proceedings of the 2014 annual conference on genetic and evolutionary computation. ACM, pp 713\u2013720","DOI":"10.1145\/2576768.2598369"},{"key":"1578_CR112","doi-asserted-by":"crossref","unstructured":"Risi S, Stanley KO (2010) Indirectly encoding neural plasticity as a pattern of local rules. In: Proceedings of the international conference on simulation of adaptive behavior, pp 533\u2013543","DOI":"10.1007\/978-3-642-15193-4_50"},{"issue":"2","key":"1578_CR113","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1162\/artl.2009.15.2.15202","volume":"15","author":"KO Stanley","year":"2009","unstructured":"Stanley KO, Ambrosio DBD, Gauci J (2009) A hypercube-based encoding for evolving large-scale neural networks. Artif Life 15(2):185\u2013212","journal-title":"Artif Life"},{"key":"1578_CR114","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.neucom.2020.12.007","volume":"432","author":"X Wang","year":"2021","unstructured":"Wang X, Jin Y, Hao K (2021) Synergies between synaptic and intrinsic plasticity in echo state networks. Neurocomputing 432:32\u201343","journal-title":"Neurocomputing"},{"issue":"4","key":"1578_CR115","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1109\/TEVC.2019.2954411","volume":"24","author":"D Guirguis","year":"2020","unstructured":"Guirguis D et al (2020) Evolutionary black-box topology optimization: challenges and promises. IEEE Trans Evol Comput 24(4):613\u2013633","journal-title":"IEEE Trans Evol Comput"},{"issue":"5","key":"1578_CR116","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1002\/cne.21974","volume":"513","author":"FAC Azevedo","year":"2010","unstructured":"Azevedo FAC, Carvalho LRB, Grinberg LT et al (2010) Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain. J Comp Neurol 513(5):532\u2013541","journal-title":"J Comp Neurol"},{"key":"1578_CR117","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1016\/j.ijthermalsci.2016.05.015","volume":"108","author":"R Boichot","year":"2016","unstructured":"Boichot R et al (2016) A genetic algorithm for topology optimization of area-to-point heat conduction problem. Int J Therm Sci 108:209\u2013217","journal-title":"Int J Therm Sci"},{"key":"1578_CR118","doi-asserted-by":"crossref","unstructured":"Aulig N, Olhofer M (2016) Evolutionary computation for topology optimization of mechanical structures: An overview of representation. In: Proceedings of the 2016 IEEE congress on evolutionary computation (CEC), pp 1948\u20131955","DOI":"10.1109\/CEC.2016.7744026"},{"key":"1578_CR119","unstructured":"Gruau F (1993) Genetic synthesis of modular neural networks. In: Proceedings of the GECCO"},{"key":"1578_CR120","unstructured":"Gruau F, Whitley D, Pyeatt L (1996) A comparison between cellular encoding and direct encoding for genetic neural networks. In: Proceedings of the 1st annual conference on genetic programming, pp 81\u201389"},{"issue":"2","key":"1578_CR121","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1007\/s10710-007-9028-8","volume":"8","author":"KO Stanley","year":"2007","unstructured":"Stanley KO (2007) Compositional pattern producing networks: a novel abstraction of development. Genet Program Evolvable Mach 8(2):131\u2013162","journal-title":"Genet Program Evolvable Mach"},{"key":"1578_CR122","doi-asserted-by":"crossref","unstructured":"Pugh JK, Stanley KO (2013) Evolving multimodal controllers with hyperneat. In: Proceedings of the 15th annual conference on Genetic and evolutionary computation, pp 735\u2013742","DOI":"10.1145\/2463372.2463459"},{"key":"1578_CR123","doi-asserted-by":"crossref","unstructured":"Fernando C, Banarse D, Reynolds M et al (2016) Convolution by evolution: differentiable pattern producing networks. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), 109-116","DOI":"10.1145\/2908812.2908890"},{"key":"1578_CR124","doi-asserted-by":"crossref","unstructured":"Stork J, Zaefferer M, Bartz-Beielstein T (2019) Improving neuroevolution efficiency by surrogate model-based optimization with phenotypic distance kernels. In: Proceedings of the international conference on the applications of evolutionary computation (Part of EvoStar), Springer, pp 504\u2013519","DOI":"10.1007\/978-3-030-16692-2_34"},{"issue":"2","key":"1578_CR125","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1162\/EVCO_a_00025","volume":"19","author":"J Lehman","year":"2011","unstructured":"Lehman J, Stanley KO (2011) Abandoning objectives: evolution through the search for novelty alone. Evol Comput 19(2):189\u2013223","journal-title":"Evol Comput"},{"key":"1578_CR126","unstructured":"Lehman J, Stanley KO (2008) Exploiting Open-endedness to solve problems through the search for novelty. In: Proceedings of the ALIFE"},{"issue":"6","key":"1578_CR127","doi-asserted-by":"publisher","first-page":"470","DOI":"10.1177\/1059712310379923","volume":"18","author":"S Risi","year":"2010","unstructured":"Risi S, Hughes CE, Stanley KO (2010) Evolving plastic neural networks with novelty search. Adapt Behav 18(6):470\u2013491","journal-title":"Adapt Behav"},{"key":"1578_CR128","doi-asserted-by":"crossref","unstructured":"Risi S, Vanderbleek SD, Hughes CE et al (2009) How novelty search escapes the deceptive trap of learning to learn. In: Proceedings of the 11th Annual conference on Genetic and evolutionary computation, pp 153-160","DOI":"10.1145\/1569901.1569923"},{"key":"1578_CR129","unstructured":"Conti E, Madhavan V, Such FP et al (2018) Improving exploration in evolution strategies for deep reinforcement learning via a population of novelty-seeking agents. In: Proceedings of the 32nd international conference on neural information processing systems (NIPS) pp 5032\u20135043"},{"key":"1578_CR130","doi-asserted-by":"crossref","unstructured":"Reisinger J, Stanley K O, Miikkulainen R (2004) Evolving reusable neural modules. In: Proceedings of the genetic and evolutionary computation conference (GECCO), Springer, pp 69\u201381","DOI":"10.1007\/978-3-540-24855-2_7"},{"key":"1578_CR131","doi-asserted-by":"crossref","unstructured":"Mouret J B, Doncieux S (2009) Evolving modular neural-networks through exaptation. In: Proceedings of the IEEE congress on evolutionary computation (CEC), IEEE, pp 1570\u20131577","DOI":"10.1109\/CEC.2009.4983129"},{"key":"1578_CR132","first-page":"937","volume":"9","author":"F Gomez","year":"2008","unstructured":"Gomez F, Schmidhuber J, Miikkulainen R (2008) Accelerated neural evolution through cooperatively coevolved synapses. J Mach Learn Res 9:937\u2013965","journal-title":"J Mach Learn Res"},{"issue":"5","key":"1578_CR133","doi-asserted-by":"publisher","first-page":"2843","DOI":"10.3233\/IFS-162095","volume":"30","author":"T Praczyk","year":"2016","unstructured":"Praczyk T (2016) Cooperative co\u2013evolutionary neural networks. J Intell Fuzzy Syst 30(5):2843\u20132858","journal-title":"J Intell Fuzzy Syst"},{"key":"1578_CR134","doi-asserted-by":"crossref","unstructured":"Liang J, Meyerson E, Miikkulainen R (2018) Evolutionary architecture search for deep multitask networks. In: Proceedings of the genetic and evolutionary computation conference (GECCO), pp 466\u2013473","DOI":"10.1145\/3205455.3205489"},{"issue":"4","key":"1578_CR135","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1004128","volume":"11","author":"KO Ellefsen","year":"2015","unstructured":"Ellefsen KO, Mouret JB, Clune J (2015) Neural modularity helps organisms evolve to learn new skills without forgetting old skills. PLoS Comput Biol 11(4):e1004128","journal-title":"PLoS Comput Biol"},{"issue":"11","key":"1578_CR136","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0187736","volume":"12","author":"R Velez","year":"2017","unstructured":"Velez R, Clune J (2017) Diffusion-based neuromodulation can eliminate catastrophic forgetting in simple neural networks. PLoS ONE 12(11):e0187736","journal-title":"PLoS ONE"},{"issue":"1","key":"1578_CR137","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1162\/evco_a_00250","volume":"28","author":"KO Ellefsen","year":"2019","unstructured":"Ellefsen KO, Huizinga J, Torresen J (2019) Guiding neuroevolution with structural objectives. Evol Comput 28(1):115\u2013140","journal-title":"Evol Comput"},{"key":"1578_CR138","doi-asserted-by":"crossref","unstructured":"Knippenberg M V, Menkovski V, Consoli S (2019) Evolutionary construction of convolutional neural networks. arXiv preprint, arXiv:1903.01895","DOI":"10.1007\/978-3-030-13709-0_25"},{"key":"1578_CR139","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1016\/j.media.2019.03.004","volume":"54","author":"P Liu","year":"2019","unstructured":"Liu P, El Basha MD, Li Y et al (2019) Deep evolutionary networks with expedited genetic algorithms for medical image denoising. Med Image Anal 54:306\u2013315","journal-title":"Med Image Anal"},{"key":"1578_CR140","unstructured":"Assun\u00e7\u00e3o F, Louren\u00e7o N, Machado P, et al (2019) Fast-DENSER++: evolving fully-trained deep artificial neural networks. arXiv preprint, arXiv:1905.02969"},{"key":"1578_CR141","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3123105","author":"S Li","year":"2021","unstructured":"Li S, Sun Y, Yen GG, Zhang M (2021) Automatic design of convolutional neural network architectures under resource constraints. IEEE Trans Neural Netw Learn Syst. https:\/\/doi.org\/10.1109\/TNNLS.2021.3123105","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"1578_CR142","doi-asserted-by":"publisher","first-page":"899","DOI":"10.1007\/s00521-021-05979-8","volume":"34","author":"G Kyriakides","year":"2022","unstructured":"Kyriakides G, Margaritis K (2022) Evolving graph convolutional networks for neural architecture search. Neural Comput Appl 34:899\u2013909","journal-title":"Neural Comput Appl"},{"key":"1578_CR143","unstructured":"Deng B, Yan J, Lin D (2017) Peephole: Predicting network performance before training. arXiv preprint arXiv:1712.03351"},{"key":"1578_CR144","doi-asserted-by":"crossref","unstructured":"Istrate R, Scheidegger F, Mariani G, Nikolopoulos D, Bekas C, Malossi A C I (2019) Tapas: Train-less accuracy predictor for architecture search. In: Proceedings of the AAAI Conference on artificial intelligence 33: 3927\u20133934","DOI":"10.1609\/aaai.v33i01.33013927"},{"issue":"3","key":"1578_CR145","doi-asserted-by":"publisher","first-page":"524","DOI":"10.1109\/TEVC.2021.3055076","volume":"25","author":"Y Sun","year":"2021","unstructured":"Sun Y, Sun X, Fang Y, Yen GG, Liu Y (2021) A novel training protocol for performance predictors of evolutionary neural architecture search algorithms. IEEE Trans Evol Comput 25(3):524\u2013536","journal-title":"IEEE Trans Evol Comput"},{"key":"1578_CR146","unstructured":"Domhan T, Springenberg J T, Hutter F (2015) Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves. In: Proceedings of the Twenty-fourth International Joint Conference on artificial intelligence"},{"key":"1578_CR147","unstructured":"Klein A, Falkner S, Springenberg TJ, Hutter F Learning curve prediction with Bayesian neural networks. In: Proceedings of the Fifth International Conference on learning representations, ICLR"},{"key":"1578_CR148","unstructured":"Baker B, Gupta O, Raskar R, Naik N (2017) Accelerating neural architecture search using performance prediction. arXiv preprint arXiv:1705.10823"},{"key":"1578_CR149","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2022.3140855","author":"H Zhang","year":"2022","unstructured":"Zhang H, Jin Y, Jin Y, Hao K (2022) Evolutionary search for complete neural network architectures with partial weight sharing. IEEE Trans Evol Comput. https:\/\/doi.org\/10.1109\/TEVC.2022.3140855","journal-title":"IEEE Trans Evol Comput"},{"key":"1578_CR150","doi-asserted-by":"crossref","unstructured":"Elsken T, Metzen J H, et al (2019) Efficient multi-objective neural architecture search via Lamarckian evolution. In: 7th International Conference on learning representations","DOI":"10.1007\/978-3-030-05318-5_3"},{"key":"1578_CR151","doi-asserted-by":"crossref","unstructured":"Liang JZ, Miikkulainen R (2015) Evolutionary bilevel optimization for complex control tasks. In: Proceedings of the 2015 annual conference on genetic and evolutionary computation, pp 871\u2013878","DOI":"10.1145\/2739480.2754732"},{"key":"1578_CR152","unstructured":"MacKay M, Vicol P, Lorraine J et al (2019) Self-tuning networks: Bilevel optimization of hyperparameters using structured best-response functions. arXiv preprint, arXiv:1903.03088"},{"key":"1578_CR153","doi-asserted-by":"crossref","unstructured":"Sinha A, Malo P, Xu P et al (2014) A bilevel optimization approach to automated parameter tuning. In: Proceedings of the GECCO","DOI":"10.1145\/2576768.2598221"},{"key":"1578_CR154","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.neucom.2017.12.049","volume":"283","author":"A Baldominos","year":"2018","unstructured":"Baldominos A, Saez Y, Isasi P (2018) Evolutionary convolutional neural networks: an application to handwriting recognition. Neurocomputing 283:38\u201352","journal-title":"Neurocomputing"},{"issue":"2","key":"1578_CR155","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1145\/3178903","volume":"3","author":"PC Huang","year":"2019","unstructured":"Huang PC, Sentis L, Lehman J et al (2019) Tradeoffs in neuroevolutionary learning-based real-time robotic task design in the imprecise computation framework. ACM Trans Cyber-Phys Syst 3(2):14","journal-title":"ACM Trans Cyber-Phys Syst"},{"issue":"6799","key":"1578_CR156","doi-asserted-by":"publisher","first-page":"974","DOI":"10.1038\/35023115","volume":"406","author":"H Lipson","year":"2000","unstructured":"Lipson H, Pollack JB (2000) Automatic design and manufacture of robotic lifeforms. Nature 406(6799):974","journal-title":"Nature"},{"key":"1578_CR157","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1016\/j.asoc.2018.05.035","volume":"70","author":"AM Dur\u00e1n-Rosal","year":"2018","unstructured":"Dur\u00e1n-Rosal AM, Fern\u00e1ndez JC, Casanova-Mateo C et al (2018) Efficient fog prediction with multi-objective evolutionary neural networks. Appl Soft Comput 70:347\u2013358","journal-title":"Appl Soft Comput"},{"key":"1578_CR158","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1016\/j.future.2018.03.040","volume":"86","author":"K Mason","year":"2018","unstructured":"Mason K, Duggan M, Barret E et al (2018) Predicting host CPU utilization in the cloud using evolutionary neural networks. Futur Gener Comput Syst 86:162\u2013173","journal-title":"Futur Gener Comput Syst"},{"issue":"2","key":"1578_CR159","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1080\/18756891.2016.1161365","volume":"9","author":"GM Khan","year":"2016","unstructured":"Khan GM, Arshad R (2016) Electricity peak load forecasting using CGP based neuro evolutionary techniques. Int J Comput Intell Syst 9(2):376\u2013395","journal-title":"Int J Comput Intell Syst"},{"key":"1578_CR160","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1016\/j.jbi.2018.11.013","volume":"89","author":"BI Grisci","year":"2019","unstructured":"Grisci BI, Feltes BC, Dorn M (2019) Neuroevolution as a tool for microarray gene expression pattern identification in cancer research. J Biomed Inf 89:122\u2013133","journal-title":"J Biomed Inf"},{"key":"1578_CR161","doi-asserted-by":"publisher","first-page":"18050","DOI":"10.1109\/ACCESS.2019.2897078","volume":"7","author":"B Abdikenov","year":"2019","unstructured":"Abdikenov B, Iklassov Z, Sharipov A et al (2019) Analytics of heterogeneous breast cancer data using neuroevolution. IEEE Access 7:18050\u201318060","journal-title":"IEEE Access"},{"key":"1578_CR162","unstructured":"Wu Y, Tan H, Jiang Z, et al. (2019) ES-CTC: A deep neuroevolution model for cooperative intelligent freeway traffic control. arXiv preprint, arXiv:1905.04083"},{"key":"1578_CR163","doi-asserted-by":"crossref","unstructured":"Trasnea B, Marina LA, Vasilcoi A, Pozna CR, Grigorescu SM (2019) GridSim: a vehicle kinematics engine for deep neuroevolutionary control in autonomous driving. In: Proceedings of the 2019 third IEEE international conference on robotic computing (IRC), IEEE, pp 443\u2013444","DOI":"10.1109\/IRC.2019.00091"},{"key":"1578_CR164","doi-asserted-by":"crossref","unstructured":"Grigorescu S, Trasnea B, Marina L et al (2019) NeuroTrajectory: a neuroevolutionary approach to local state trajectory learning for autonomous vehicles. arXiv preprint, arXiv:1906.10971","DOI":"10.1109\/LRA.2019.2926224"},{"key":"1578_CR165","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1016\/j.neucom.2016.09.092","volume":"228","author":"F Han","year":"2017","unstructured":"Han F, Zhao MR, Zhang JM et al (2017) An improved incremental constructive single-hidden-layer feedforward networks for extreme learning machine based on particle swarm optimization. Neurocomputing 228:133\u2013142","journal-title":"Neurocomputing"},{"key":"1578_CR166","unstructured":"ElSaid A, Ororbia A, Desell T (2019) The ant swarm neuro-evolution procedure for optimizing recurrent networks. arXiv preprint, arXiv:1909.11849"},{"key":"1578_CR167","doi-asserted-by":"crossref","unstructured":"Zhu W, Yeh WC, Chen J, Chen D, Li A, Lin Y (2019) Evolutionary convolutional neural networks using ABC. In: Proceedings of the 2019 11th international conference on machine learning and computing (ICMLC), pp 156\u2013162","DOI":"10.1145\/3318299.3318301"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-022-01578-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-022-01578-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-022-01578-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,26]],"date-time":"2024-09-26T19:37:18Z","timestamp":1727379438000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-022-01578-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,10]]},"references-count":167,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2022,10]]}},"alternative-id":["1578"],"URL":"https:\/\/doi.org\/10.1007\/s13042-022-01578-8","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,10]]},"assertion":[{"value":"3 December 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 May 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 June 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}