{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T01:29:59Z","timestamp":1768267799768,"version":"3.49.0"},"reference-count":139,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2019,3,27]],"date-time":"2019-03-27T00:00:00Z","timestamp":1553644800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100003176","name":"Ministerio de Educaci\u00f3n, Cultura y Deporte","doi-asserted-by":"publisher","award":["FPU13\/03917"],"award-info":[{"award-number":["FPU13\/03917"]}],"id":[{"id":"10.13039\/501100003176","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2020,1]]},"DOI":"10.1007\/s00521-019-04160-6","type":"journal-article","created":{"date-parts":[[2019,3,27]],"date-time":"2019-03-27T20:14:17Z","timestamp":1553717657000},"page":"519-545","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["On the automated, evolutionary design of neural networks: past, present, and future"],"prefix":"10.1007","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8906-7572","authenticated-orcid":false,"given":"Alejandro","family":"Baldominos","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yago","family":"Saez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pedro","family":"Isasi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,3,27]]},"reference":[{"key":"4160_CR1","unstructured":"Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, Ghemawat S, Irving G, Isard M, Kudlur M, Levenberg J, Monga R, Moore S, Murray DG, Steiner B, Tucker P, Vasudevan V, Warden P, Wicke M, Yu Y, Zheng X (2016) TensorFlow: a system for large-scale machine learning. In: 12th USENIX symposium on operating systems design and implementation, pp 265\u2013283"},{"issue":"1","key":"4160_CR2","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1109\/72.265960","volume":"5","author":"PJ Angeline","year":"1994","unstructured":"Angeline PJ, Saunders GM, Pollack JB (1994) An evolutionary algorithm that constructs recurrent neural networks. IEEE Trans Neural Netw 5(1):54\u201365","journal-title":"IEEE Trans Neural Netw"},{"key":"4160_CR3","doi-asserted-by":"crossref","unstructured":"Assun\u00e7\u00e3o F, Louren\u00e7o N, Machado P, Ribeiro B (2018) DENSER: deep evolutionary network structured representation. Genet Program Evol Mach (in press)","DOI":"10.1007\/s10710-018-9339-y"},{"key":"4160_CR4","unstructured":"AWS: Amazon EC2 P3 Instances (2017). https:\/\/aws.amazon.com\/es\/ec2\/instance-types\/p3\/ . Last queried on 14 Nov 2017"},{"key":"4160_CR5","unstructured":"Baird L (1999) Reinforcement learning through gradient descent. Ph.D. thesis, School of Computer Science, Carnegie Mellon University"},{"key":"4160_CR6","unstructured":"Balakrishnan K, Honavar V (1995) Evolutionary design of neural architectures\u2014a preliminary taxonomy and guide to literature. Technical report, Iowa State University. Paper 26"},{"key":"4160_CR7","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":"4","key":"4160_CR8","doi-asserted-by":"publisher","first-page":"1288","DOI":"10.3390\/s18041288","volume":"18","author":"A Baldominos","year":"2018","unstructured":"Baldominos A, Saez Y, Isasi P (2018) Evolutionary design of convolutional neural networks for human activity recognition in sensor-rich environments. Sensors 18(4):1288","journal-title":"Sensors"},{"key":"4160_CR9","doi-asserted-by":"crossref","unstructured":"Baldominos A, Saez Y, Isasi P (2018) Model selection in committees of evolved convolutional neural networks using genetic algorithms. In: Intelligent data engineering and automated learning\u2014IDEAL 2018. Lecture Notes in Computer Science, vol 11314. Springer, pp 364\u2013373","DOI":"10.1007\/978-3-030-03493-1_39"},{"issue":"1","key":"4160_CR10","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1162\/neco.1989.1.1.151","volume":"1","author":"EB Baum","year":"1989","unstructured":"Baum EB, Haussler D (1989) What size net gives valid generalization? Neural Comput 1(1):151\u2013160","journal-title":"Neural Comput"},{"key":"4160_CR11","first-page":"511","volume-title":"Artificial life II","author":"RK Belew","year":"1991","unstructured":"Belew RK, McInerney K, Schraudolph NN (1991) Evolving networks: using the genetic algorithm with connectionist learning. In: Langton CG, Taylor C, Farmer JD, Rasmussen S (eds) Artificial life II. Addison-Wesley, MA, pp 511\u2013547"},{"key":"4160_CR12","doi-asserted-by":"crossref","unstructured":"Bergstra J, Breuleux O, Bastien F, Lamblin P, Pascanu R, Desjardins G, Turian J, Warde-Farley D, Bengio Y (2010) Theano: a CPU and GPU math compiler in Python. In: 9th Python in science conference","DOI":"10.25080\/Majora-92bf1922-003"},{"issue":"1","key":"4160_CR13","first-page":"115","volume":"28","author":"J Bergstra","year":"2013","unstructured":"Bergstra J, Yamins D, Cox D (2013) Making a science of model search: hyperparameter optimization in hundreds of dimensions for vision architectures. J Mach Learn Res 28(1):115\u2013123","journal-title":"J Mach Learn Res"},{"issue":"3","key":"4160_CR14","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1145\/937503.937505","volume":"35","author":"C Blum","year":"2003","unstructured":"Blum C, Roli A (2003) Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput Surv 35(3):268\u2013308","journal-title":"ACM Comput Surv"},{"key":"4160_CR15","doi-asserted-by":"crossref","unstructured":"Bochinski E, Senst T, Sikora T (2017) Hyper-parameter optimization for convolutional neural network committees based on evolutionary algorithms. In: 2017 IEEE international conference on image processing, pp 3924\u20133928","DOI":"10.1109\/ICIP.2017.8297018"},{"key":"4160_CR16","unstructured":"Canziani A, Paszke A, Culurciello E (2017) An analysis of deep neural network models for practical applications. arXiv:1605.07678"},{"key":"4160_CR17","unstructured":"Caruana R (1993) Generalization vs. net size. NIPS Tutorial. Denver, CO"},{"key":"4160_CR18","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/B978-1-4832-1448-1.50014-7","volume-title":"Connectionist Models","author":"David J. Chalmers","year":"1991","unstructured":"Chalmers DJ (1990) The evolution of learning: an experiment in genetic connectionism. In: 1990 Connectionist Models Summer School, pp 81\u201390"},{"key":"4160_CR19","unstructured":"Chetlur S, Woolley C, Vandermersch P, Cohen J, Tran J, Catanzaro B, Shelhamer E (2014) cuDNN: efficient primitives for deep learning. arXiv:1410.0759"},{"key":"4160_CR20","doi-asserted-by":"crossref","unstructured":"Cho K, Van\u00a0Merri\u00ebnboer B, Bahdanau D, Bengio Y (2014) On the properties of neural machine translation: encoder\u2013decoder approaches. arXiv:1409.1259","DOI":"10.3115\/v1\/W14-4012"},{"issue":"1","key":"4160_CR21","first-page":"24","volume":"2","author":"A Choudhary","year":"2010","unstructured":"Choudhary A, Rishi R, Dhaka VS, Ahlawat S (2010) Influence of introducing an additional hidden layer on the character recognition capability of a BP neural network having one hidden layer. Int J Eng Technol 2(1):24\u201328","journal-title":"Int J Eng Technol"},{"key":"4160_CR22","unstructured":"Cramer NL (1985) A representation for the adaptive generation of simple sequential programs. In: 1st international conference on genetic algorithms and their applications, pp 183\u2013187"},{"key":"4160_CR23","unstructured":"Cui X, Zhang W, T\u00fcske Z, Picheny M (2018) Evolutionary stochastic gradient descent for optimization of deep neural networks. In: Advances in neural information processing systems 31. NIPS Proceedings"},{"key":"4160_CR24","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/BF02551274","volume":"2","author":"G Cybenko","year":"1989","unstructured":"Cybenko G (1989) Approximation by superpositions of a sigmoidal function. Math Control Signals Syst 2:303\u2013314","journal-title":"Math Control Signals Syst"},{"key":"4160_CR25","doi-asserted-by":"crossref","unstructured":"Dasgupta D, McGregor DR (1992) Designing application-specific neural networks using the structured genetic algorithm. In: International workshop on combinations of genetic algorithms and neural networks, pp 87\u201396","DOI":"10.1109\/COGANN.1992.273946"},{"key":"4160_CR26","unstructured":"Davison J (2017) DEvol: Automated deep neural network design via genetic programming. https:\/\/github.com\/joeddav\/devol . Last visited on 01 July 2017"},{"key":"4160_CR27","unstructured":"de Garis H (1992) Steerable GenNETS: the genetic programming of steerable behavior in GenNETS. In: Towards a practice of autonomous systems, pp 272\u2013281"},{"key":"4160_CR28","unstructured":"Dean J, H\u00f6lzle U (2017) Build and train machine learning models on our new Google Cloud TPUs. https:\/\/www.blog.google\/topics\/google-cloud\/google-cloud-offer-tpus-machine-learning\/ . Published on 17 May 2017"},{"key":"4160_CR29","doi-asserted-by":"crossref","unstructured":"Desell T (2017) Large scale evolution of convolutional neural networks using volunteer computing. In: 2017 genetic and evolutionary computation conference companion, pp 127\u2013128","DOI":"10.1145\/3067695.3076002"},{"issue":"3","key":"4160_CR30","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, Yu J, Jin F (2013) Evolutionary artificial neural networks: a review. Artif Intell Rev 39(3):251\u2013260","journal-title":"Artif Intell Rev"},{"issue":"10","key":"4160_CR31","doi-asserted-by":"publisher","first-page":"e1002236","DOI":"10.1371\/journal.pcbi.1002236","volume":"7","author":"JA Edlund","year":"2011","unstructured":"Edlund JA, Chaumont N, Hintze A, Koch C, Tononi G, Adami C (2011) Integrated information increases with fitness in the evolution of animats. PLOS Comput Biol 7(10):e1002236","journal-title":"PLOS Comput Biol"},{"key":"4160_CR32","doi-asserted-by":"crossref","unstructured":"Elias JG (1992) Genetic generation of connection patterns for a dynamic artificial neural network. In: International workshop on combinations of genetic algorithms and neural networks, pp 38\u201354","DOI":"10.1109\/COGANN.1992.273949"},{"key":"4160_CR33","series-title":"Morgan Kaufmann","first-page":"524","volume-title":"Advances in neural information processing systems","author":"SE Fahlman","year":"1990","unstructured":"Fahlman SE, Lebiere C (1990) The cascade-correlation learning architecture. In: Touretzky DS (ed) Advances in neural information processing systems, vol 2. Morgan Kaufmann. Los Altos, CA, pp 524\u2013532"},{"key":"4160_CR34","doi-asserted-by":"crossref","unstructured":"Fernando C, Banarse D, Reynolds M, Besse F, Pfau D, Jaderberg M, Lanctot M, Wierstra D (2016) Convolution by evolution: differentiable pattern producing networks. In: 2016 genetic and evolutionary computation conference, pp 109\u2013116","DOI":"10.1145\/2908812.2908890"},{"issue":"1","key":"4160_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12065-007-0002-4","volume":"1","author":"D Floreano","year":"2008","unstructured":"Floreano D, D\u00fcrr P, Mattiussi C (2008) Neuroevolution: from architectures to learning. Evol Intell 1(1):1\u201347","journal-title":"Evol Intell"},{"key":"4160_CR36","volume-title":"Artificial intelligence through simulated evolution","author":"LJ Foley","year":"1966","unstructured":"Foley LJ, Owens AJ, Walsh MJ (1966) Artificial intelligence through simulated evolution. Wiley, Hoboken"},{"issue":"3","key":"4160_CR37","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1108\/eb005587","volume":"10","author":"R Forsyth","year":"1981","unstructured":"Forsyth R (1981) BEAGLE a Darwinian approach to pattern recognition. Kybernetes 10(3):159\u2013166","journal-title":"Kybernetes"},{"issue":"2","key":"4160_CR38","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1162\/neco.1990.2.2.198","volume":"2","author":"M Frean","year":"1990","unstructured":"Frean M (1990) The upstart algorithm: a method for constructing and training feedforward neural networks. Neural Comput 2(2):198\u2013209","journal-title":"Neural Comput"},{"key":"4160_CR39","unstructured":"Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feed forward neural networks. In: 13th international conference on artificial intelligence and statistics, vol 9. JMLR Proceedings, pp 249\u2013256"},{"key":"4160_CR40","first-page":"425740","volume":"2013","author":"K Gnana Sheela","year":"2013","unstructured":"Gnana Sheela K, Deepa SN (2013) Review on methods to fix number of hidden neurons in neural networks. Math Probl Eng 2013:425740","journal-title":"Math Probl Eng"},{"key":"4160_CR41","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"},{"key":"4160_CR42","unstructured":"Gruau F (1994) Neural network synthesis using cellular encoding and the genetic algorithm. Ph.D. thesis, Laboratoire de l\u2019Informatique du Parall\u00e9lisme, Ecole Normale Sup\u00e9rieure de Lyon"},{"key":"4160_CR43","unstructured":"Hammerla NY, Halloran S, Pl\u00f6tz T (2016) Deep, convolutional, and recurrent models for human activity recognition using wearables. In: 25th international conference on artificial intelligence, pp 1533\u20131540"},{"key":"4160_CR44","doi-asserted-by":"crossref","unstructured":"Hancock PJB (1992) Genetic algorithms and permutation problems: a comparison of recombination operators for neural net structure specification. In: International Workshop on combinations of genetic algorithms and neural networks, pp 108\u2013122","DOI":"10.1109\/COGANN.1992.273944"},{"key":"4160_CR45","doi-asserted-by":"crossref","unstructured":"Hansen N (2006) The CMA evolution strategy: a comparing review. In: Towards a new evolutionary computation. Springer, pp 75\u2013102","DOI":"10.1007\/3-540-32494-1_4"},{"key":"4160_CR46","unstructured":"Harp SA, Samad T, Guha A (1989) Towards the genetic synthesis of neural networks. In: 3rd international conference on genetic algorithms, pp 360\u2013369"},{"key":"4160_CR47","unstructured":"Harp SA, Samad T, Guha A (1990) Designing application-specific neural networks using the genetic algorithm. In: Advances NIPS 2. Morgan Kaufmann, pp 447\u2013454"},{"key":"4160_CR48","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition. IEEE","DOI":"10.1109\/CVPR.2016.90"},{"issue":"6","key":"4160_CR49","doi-asserted-by":"publisher","first-page":"e1002063","DOI":"10.1371\/journal.pcbi.1002063","volume":"7","author":"AM Hermundstad","year":"2011","unstructured":"Hermundstad AM, Brown KS, Bassett DS, Carlson JM (2011) Learning, memory, and the role of neural network architecture. PLOS Comput Biol 7(6):e1002063","journal-title":"PLOS Comput Biol"},{"key":"4160_CR50","unstructured":"Hintzelab. MABE: Modular Agent Based Evolution Framework (2017). https:\/\/github.com\/Hintzelab\/MABE . Last visited on 27 June 2017"},{"issue":"1","key":"4160_CR51","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/0893-6080(91)90032-Z","volume":"4","author":"Y Hirose","year":"1991","unstructured":"Hirose Y, Yamashita K, Hijiya S (1991) Back-propagation algorithm which varies the number of hidden units. Neural Netw 4(1):61\u201366","journal-title":"Neural Netw"},{"issue":"8","key":"4160_CR52","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"},{"key":"4160_CR53","volume-title":"Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence","author":"JH Holland","year":"1975","unstructured":"Holland JH (1975) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. University of Michigan Press, Ann Arbor"},{"key":"4160_CR54","doi-asserted-by":"crossref","unstructured":"Iba H (2018) Evolutionary approach to deep learning. In: Evolutionary approach to machine learning and deep neural networks. Springer, pp 77\u2013104","DOI":"10.1007\/978-981-13-0200-8_3"},{"key":"4160_CR55","doi-asserted-by":"crossref","unstructured":"Igel C (2003) Neuroevolution for reinforcement learning using evolution strategies. In: 2003 IEEE congress on evolutionary computation, pp 2588\u20132595","DOI":"10.1109\/CEC.2003.1299414"},{"key":"4160_CR56","unstructured":"Karpathy A (2015) The unreasonable effectiveness of recurrent neural networks. http:\/\/karpathy.github.io\/2015\/05\/21\/rnn-effectiveness\/ . Published on 21 May 2015"},{"key":"4160_CR57","doi-asserted-by":"crossref","unstructured":"Karunanithi N, Das R, Whitley D (1992) Genetic cascade learning for neural networks. In: International workshop on combinations of genetic algorithms and neural networks, pp 134\u2013145","DOI":"10.1109\/COGANN.1992.273942"},{"key":"4160_CR58","doi-asserted-by":"crossref","unstructured":"Kassahun Y, Edgington M, Metzen JH, Sommer G, Kirchner F (2007) Common genetic encoding for both direct and indirect encodings of networks. In: 9th annual conference on genetic and evolutionary computation, pp 1029\u20131036","DOI":"10.1145\/1276958.1277162"},{"key":"4160_CR59","unstructured":"Kassahun Y, Sommer G (2005) Efficient reinforcement learning through evolutionary acquisition of neural topologies. In: 13th European symposium on artificial neural networks, pp 259\u2013266"},{"key":"4160_CR60","first-page":"461","volume":"4","author":"H Kitano","year":"1990","unstructured":"Kitano H (1990) Designing neural networks using genetic algorithms with graph generation system. Complex Syst 4:461\u2013476","journal-title":"Complex Syst"},{"key":"4160_CR61","doi-asserted-by":"crossref","unstructured":"Koutn\u00edk J, Schmidhuber J, Gomez F (2014) Evolving deep unsupervised convolutional networks for vision-based reinforcement learning. In: 2014 annual conference on genetic and evolutionary computation, pp 541\u2013548","DOI":"10.1145\/2576768.2598358"},{"key":"4160_CR62","unstructured":"Koza JR (1989) Hierarchical genetic algorithms operating on populations of computer programs. In: 11th international joint conference on artificial intelligence, pp 7768\u2013774"},{"key":"4160_CR63","volume-title":"Genetic programming: the movie","author":"JR Koza","year":"1992","unstructured":"Koza JR, Rice JP (1992) Genetic programming: the movie. MIT Press, Cambridge"},{"key":"4160_CR64","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1007\/978-3-319-77538-8_27","volume-title":"EvoApplications 2018: applications of evolutionary computation","author":"O Kramer","year":"2018","unstructured":"Kramer O (2018) Evolution of convolutional highway networks. In: Sim K, Kaufmann P (eds) EvoApplications 2018: applications of evolutionary computation, vol 10784. Lecture Notes in Computer Science. Springer, Berlin, pp 395\u2013404"},{"key":"4160_CR65","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Advances NIPS 25. NIPS Proceedings, pp 1097\u20131105"},{"key":"4160_CR66","doi-asserted-by":"crossref","unstructured":"Larochelle H, Erhan D, Courville A, Bergstra J, Bengio Y (2007) An empirical evaluation of deep architectures on problems with many factors of variation. In: 24th international conference on machine learning, pp 473\u2013480","DOI":"10.1145\/1273496.1273556"},{"key":"4160_CR67","volume-title":"What size neural network gives optimal generalization?","author":"S Lawrence","year":"1996","unstructured":"Lawrence S, Giles CL, Tsoi AC (1996) What size neural network gives optimal generalization?. Technical report, Institute for Advanced Computer Studies, University of Maryland, Convergence properties of backpropagation"},{"key":"4160_CR68","first-page":"255","volume-title":"The handbook of brain theory and neural network","author":"Y LeCun","year":"1998","unstructured":"LeCun Y, Bengio Y (1998) Convolutional networks for images, speech, and time series. In: Arbib MA (ed) The handbook of brain theory and neural network. MIT Press, MA, USA, pp 255\u2013258"},{"issue":"11","key":"4160_CR69","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278\u20132324","journal-title":"Proc IEEE"},{"key":"4160_CR70","unstructured":"LeCun Y, Denker JS, Solla SA (1990) Optimal brain damage. In: Advances NIPS 2. Morgan Kaufmann, pp 598\u2013605"},{"key":"4160_CR71","doi-asserted-by":"crossref","unstructured":"Lindgren K, Nilsson A, Nordahl MG, Rade I (1992) Regular language inference using evolving neural networks. In: International workshop on combinations of genetic algorithms and neural networks, pp 75\u201386","DOI":"10.1109\/COGANN.1992.273947"},{"issue":"2","key":"4160_CR72","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1007\/BF01931367","volume":"16","author":"S Linnainmaa","year":"1976","unstructured":"Linnainmaa S (1976) Taylor expansion of the accumulated rounding error. BIT Numer Math 16(2):146\u2013160","journal-title":"BIT Numer Math"},{"key":"4160_CR73","unstructured":"Lipton ZC, Berkowitz J (2015) A critical review of recurrent neural networks for sequence learning. arXiv:1506.00019"},{"key":"4160_CR74","unstructured":"Liu H, Simonyan K, Vinyals O, Fernando C, Kavukcuoglu K (2018) Hierarchical representations for efficient architecture search. In: 6th international conference on learning representations"},{"key":"4160_CR75","unstructured":"Loshchilov I, Hutter F (2016) CMA-ES for hyperparameter optimization of deep neural networks. In: 2016 international conference on learning representations workshop track"},{"key":"4160_CR76","unstructured":"Lu Z, Whalen I, Boddeti V, Dhebar Y, Deb K, Goodman E, Banzhaf W (2018) NSGA-NET: a multi-objective genetic algorithm for neural architecture search. arXiv:1810.03522"},{"key":"4160_CR77","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1146\/annurev.es.09.110178.000335","volume":"9","author":"J Maynard Smith","year":"1978","unstructured":"Maynard Smith J (1978) Optimization theory in evolution. Ann Rev Ecol Syst 9:31\u201356","journal-title":"Ann Rev Ecol Syst"},{"issue":"1","key":"4160_CR78","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/0893-6080(91)90031-Y","volume":"4","author":"JWL Merrill","year":"1991","unstructured":"Merrill JWL, Port RF (1991) Fractally configured neural networks. Neural Netw 4(1):53\u201360","journal-title":"Neural Netw"},{"key":"4160_CR79","unstructured":"Miihlenbein H, Kindermann J (1989) The dynamics of evolution and learning\u2014towards genetic neural networks. In: Pfeifer R, Schreter Z, Fogelman-Souli\u00e9 F, Steels L (eds)\u00a0Connectionism in perspective. Elsevier, pp 173\u2013197"},{"key":"4160_CR80","doi-asserted-by":"crossref","unstructured":"Miikkulainen R (2017) Neuroevolution. In: Sammut C, Webb GI (eds) Encyclopedia of machine learning and data mining. Springer, pp 899\u2013904","DOI":"10.1007\/978-1-4899-7687-1_594"},{"key":"4160_CR81","doi-asserted-by":"publisher","first-page":"1281","DOI":"10.1007\/978-1-4899-7687-1_843","volume-title":"Encyclopedia of machine learning and data mining","author":"R Miikkulainen","year":"2017","unstructured":"Miikkulainen R (2017) Topology of a neural network. In: Sammut C, Webb GI (eds) Encyclopedia of machine learning and data mining. Springer, Boston, MA, pp 1281\u20131281"},{"key":"4160_CR82","unstructured":"Miikkulainen R, Liang J, Meyerson E, Rawal A, Fink D, Francon O, Raju B, Shahrzad H, Navruzyan A, Duffy N, Hodjat B (2017) Evolving deep neural networks. arXiv:1703.00548"},{"key":"4160_CR83","unstructured":"Miller GF, Todd P, Hedge SU (1989) Designing neural networks using genetic algorithms. In: 3rd international conference on genetic algorithms, pp 379\u2013384"},{"key":"4160_CR84","unstructured":"Minsky ML (1954) Theory of neural-analog reinforcement systems and its application to the brain-model problem. Ph.D. thesis, Princeton University"},{"key":"4160_CR85","volume-title":"Perceptrons: an introduction to computational geometry","author":"ML Minsky","year":"1969","unstructured":"Minsky ML, Papert SA (1969) Perceptrons: an introduction to computational geometry. MIT Press, Cambridge"},{"key":"4160_CR86","unstructured":"Mishkin D, Sergievskiy N, Matas J (2016) Systematic evaluation of CNN advances on the ImageNet. arXiv:1606.02228"},{"key":"4160_CR87","unstructured":"Montana DJ, Davis L (1989) Training feedforward neural networks using genetic algorithms. In: 11th joint international conference on artificial intelligence, pp 762\u2013767"},{"key":"4160_CR88","unstructured":"Mozer MC, Smolensky P (1989) Skeletonization: a technique for trimming the fat from a network via relevance assessment. In: Advances NIPS 1. Morgan Kaufmann, pp 107\u2013115"},{"key":"4160_CR89","unstructured":"New York Times (1958). New Navy device learns by doing; psychologist shows embryo of computer designed to read and grow wiser. http:\/\/www.nytimes.com\/1958\/07\/08\/archives\/new-navy-device-learns-by-doing-psychologist-shows-embryo-of.html"},{"key":"4160_CR90","unstructured":"NVIDIA: The world\u2019s most efficient supercomputer for AI and deep learning (2017). http:\/\/images.nvidia.com\/content\/pdf\/infographic\/dgx-saturnv-infographic.pdf . Last visited on 15 July 2017"},{"issue":"4","key":"4160_CR91","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1016\/S0893-6080(05)80061-9","volume":"6","author":"SV Odri","year":"1993","unstructured":"Odri SV, Petrovacki DP, Krstonosic GA (1993) Evolutional development of a multilevel neural network. Neural Netw 6(4):583\u2013595","journal-title":"Neural Netw"},{"key":"4160_CR92","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1038\/348027a0","volume":"348","author":"GA Parker","year":"1990","unstructured":"Parker GA, Maynard Smith J (1990) Optimality theory in evolutionary biology. Nature 348:27\u201333","journal-title":"Nature"},{"key":"4160_CR93","unstructured":"Prechelt L (1995) Neural Net FAQ . https:\/\/www.cs.cmu.edu\/Groups\/AI\/util\/html\/faqs\/ai\/neural\/faq.html . Last modified on 23 Feb 1995"},{"key":"4160_CR94","doi-asserted-by":"crossref","unstructured":"Prellberg J, Kramer O (2018) Lamarckian evolution of convolutional neural networks. arXiv:1806.08099","DOI":"10.1007\/978-3-319-99259-4_34"},{"key":"4160_CR95","doi-asserted-by":"crossref","unstructured":"Prellberg J, Kramer O (2018) Limited evaluation evolutionary optimization of large neural networks. arXiv:1806.09819","DOI":"10.1007\/978-3-030-00111-7_23"},{"key":"4160_CR96","first-page":"40","volume":"3","author":"J Pugh","year":"2016","unstructured":"Pugh J, Soros L, Stanley K (2016) Quality diversity: a new frontier for evolutionary computation. Front Robot Artif Intell 3:40","journal-title":"Front Robot Artif Intell"},{"key":"4160_CR97","unstructured":"Real E, Aggarwal A, Huang Y, Le QV (2018) Regularized evolution for image classifier architecture search. arXiv:1802.01548"},{"key":"4160_CR98","unstructured":"Real E, Moore S, Selle A, Saxena S, Leon-Suematsu Y, Tan J, Le QV, Kurakin A (2017) Large-scale evolution of image classifiers. In: Proceedings of the 34th international conference on machine learning, vol 70. JMLR Proceedings"},{"key":"4160_CR99","unstructured":"Rechenberg I (1971) Evolutionsstrategie \u2013 optimierung technischer systeme nach prinzipien der biologischen evolution. Ph.D. thesis, Technische Universit\u00e4t Berlin"},{"issue":"4","key":"4160_CR100","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1162\/ARTL_a_00071","volume":"18","author":"S Risi","year":"2012","unstructured":"Risi S, Stanley KO (2012) An enhanced hypercube-based encoding for evolving the placement, density, and connectivity of neurons. Artif Life 18(4):331\u2013363","journal-title":"Artif Life"},{"key":"4160_CR101","volume-title":"The perceptron\u2013a perceiving and recognizing automaton","author":"F Rosenblatt","year":"1957","unstructured":"Rosenblatt F (1957) The perceptron\u2013a perceiving and recognizing automaton. Technical report, Cornell Aeronautical Laboratory"},{"key":"4160_CR102","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1038\/323533a0","volume":"323","author":"D Rumelhart","year":"1986","unstructured":"Rumelhart D, Hinton G, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323:533\u2013536","journal-title":"Nature"},{"issue":"3","key":"4160_CR103","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC (2015) ImageNet large scale visual recognition challenge. Int J Comput Vis 115(3):211\u2013252","journal-title":"Int J Comput Vis"},{"key":"4160_CR104","volume-title":"Encyclopedia of machine learning and data mining","year":"2017","unstructured":"Sammut C, Webb GI (eds) (2017) Encyclopedia of machine learning and data mining. Springer, Berlin"},{"issue":"1\u20133","key":"4160_CR105","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1016\/0167-2789(90)90078-4","volume":"42","author":"JD Schaffer","year":"1990","unstructured":"Schaffer JD, Caruana RA, Eshelman LJ (1990) Using genetic search to exploit the emergent behavior of neural networks. Phys D Nonlinear Phenom 42(1\u20133):244\u2013248","journal-title":"Phys D Nonlinear Phenom"},{"key":"4160_CR106","doi-asserted-by":"crossref","unstructured":"Schaffer JD, Whitley D, Eshelman LJ (1992) Combinations of genetic algorithms and neural networks: a survey of the state of the art. In: International workshop on combinations of genetic algorithms and neural networks, pp 1\u201337","DOI":"10.1109\/COGANN.1992.273950"},{"key":"4160_CR107","doi-asserted-by":"crossref","unstructured":"Schiffmann W, Joost M, Werner R (1991) Performance evaluation of evolutionarily created neural network topologies. In: Schwefel HP, M\u00e4nner R (eds) Parallel Problem Solving from Nature. PPSN 1990. Lecture Notes in Computer Science, vol 496. Springer, pp 274\u2013283","DOI":"10.1007\/BFb0029764"},{"key":"4160_CR108","doi-asserted-by":"crossref","unstructured":"Scholz M (1991) A learning strategy for neural networks based on a modified evolutionary strategy. In:\u00a0Schwefel HP,\u00a0M\u00e4nner R (eds)\u00a0Parallel Problem Solving from Nature. PPSN 1990. Lecture Notes in Computer Science, vol 496. Springer, pp 314\u2013318","DOI":"10.1007\/BFb0029770"},{"key":"4160_CR109","unstructured":"Schwefel HP (1974) Evolutionsstrategie und numerische optimierung. Ph.D. thesis, Technische Universit\u00e4t Berlin"},{"issue":"3","key":"4160_CR110","doi-asserted-by":"publisher","first-page":"171","DOI":"10.3233\/HIS-2007-4304","volume":"4","author":"NT Siebel","year":"2007","unstructured":"Siebel NT, Sommer G (2007) Evolutionary reinforcement learning of artificial neural networks. Int J Hybrid Intell Syst 4(3):171\u2013183","journal-title":"Int J Hybrid Intell Syst"},{"issue":"1","key":"4160_CR111","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/0893-6080(91)90033-2","volume":"4","author":"J Sietsma","year":"1991","unstructured":"Sietsma J, Dow RJF (1991) Creating artificial neural networks that generalize. Neural Netw 4(1):67\u201379","journal-title":"Neural Netw"},{"key":"4160_CR112","unstructured":"Snoek J, Larochelle H, Adams RP (2012) Practical Bayesian optimization of machine learning algorithms. In: Advances in neural information processing systems 25. NIPS Proceedings, pp 2951\u20132959"},{"issue":"1","key":"4160_CR113","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton GE, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929\u20131958","journal-title":"J Mach Learn Res"},{"issue":"2","key":"4160_CR114","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, D\u2019Ambrosio DB, Gauci J (2009) A hypercube-based encoding for evolving large-scale neural networks. Artif Life 15(2):185\u2013212","journal-title":"Artif Life"},{"issue":"2","key":"4160_CR115","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. Evolut Comput 10(2):99\u2013127","journal-title":"Evolut Comput"},{"key":"4160_CR116","unstructured":"Such FP, Madhavan V, Conti E, Lehman J, Stanley KO, Clune J (2018) Deep neuroevolution: genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning. arXiv:1712.06567"},{"key":"4160_CR117","doi-asserted-by":"crossref","unstructured":"Suganuma M, Shirakawa S, Nagao T (2017) A genetic programming approach to designing convolutional neural network architectures. In: 2017 genetic and evolutionary computation conference companion, pp 497\u2013504","DOI":"10.1145\/3071178.3071229"},{"key":"4160_CR118","unstructured":"Sun Y, Xue B, Zhang M (2017) Evolving deep convolutional neural networks for image classification. arXiv:1710.10741"},{"key":"4160_CR119","unstructured":"Sun Y, Xue B, Zhang M (2018) Automatically evolving cnn architectures based on blocks. arXiv:1810.11875"},{"key":"4160_CR120","unstructured":"Szegedy C, Ioffe S, Vanhoucke V, Alemi A (2016) Inception-v4, Inception-ResNet and the impact of residual connections on learning. In: 31st AAAI conference on artificial intelligence, pp 4278\u20134284"},{"key":"4160_CR121","doi-asserted-by":"publisher","DOI":"10.1002\/9780470496916","volume-title":"Metaheuristics: from design to implementation","author":"EG Talbi","year":"2009","unstructured":"Talbi EG (2009) Metaheuristics: from design to implementation. Wiley, Hoboken"},{"key":"4160_CR122","doi-asserted-by":"crossref","unstructured":"Tirumala SS, Ali S, Ramesh CP (2016) Evolving deep neural networks: a new prospect. In: 12th international conference on natural computation, fuzzy systems and knowledge discovery, pp 69\u201374","DOI":"10.1109\/FSKD.2016.7603153"},{"key":"4160_CR123","unstructured":"Torreele J (1991) Temporal processing with recurrent networks: an evolutionary approach. In: 4th international conference on genetic algorithms, pp 555\u2013561"},{"key":"4160_CR124","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1093\/mind\/LIX.236.433","volume":"59","author":"AM Turing","year":"1950","unstructured":"Turing AM (1950) Computing machinery and intelligence. Mind 59:433\u2013460","journal-title":"Mind"},{"key":"4160_CR125","unstructured":"Verbancsics P, Harguess J (2013) Generative neuroevolution for deep learning. arXiv:1312.5355"},{"key":"4160_CR126","doi-asserted-by":"crossref","unstructured":"Verbancsics P, Harguess J (2015) Image classification using generative neuroevolution for deep learning. In: 2015 IEEE winter conference on applications of computer vision, pp 488\u2013493","DOI":"10.1109\/WACV.2015.71"},{"key":"4160_CR127","volume-title":"Automatic generation of neural network architecture using evolutionary computation, advances fuzzy systems\u2013application and theory","author":"E Vonk","year":"1997","unstructured":"Vonk E, Jain LC, Johnson RP (1997) Automatic generation of neural network architecture using evolutionary computation, advances fuzzy systems\u2013application and theory, vol 14. World Scientific Publishing, Singapore"},{"key":"4160_CR128","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1109\/ETD.1995.403479","volume":"2000","author":"E Vonk","year":"1995","unstructured":"Vonk E, Jain LC, Veelenturf LPJ, Johnson RP (1995) Automatic generation of a neural network architecture using evolutionary computation. Electronic Technology Directions to the Year 2000:144\u2013149","journal-title":"Electronic Technology Directions to the Year"},{"key":"4160_CR129","unstructured":"Wang B, Sun Y, Xue B, Zhang M (2018) A hybrid DE approach to designing CNN for image classification. In: 31st Australasian joint conference on artificial intelligence"},{"issue":"2","key":"4160_CR130","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1016\/0893-6080(94)90023-X","volume":"7","author":"Z Wang","year":"1994","unstructured":"Wang Z, Di Massimo C, Tham MT, Morris AJ (1994) A procedure for determining the topology of multilayer feedforward neural networks. Neural Netw 7(2):291\u2013300","journal-title":"Neural Netw"},{"key":"4160_CR131","unstructured":"Werbos PJ (1974) Beyond regression: new tools for prediction and analysis in the behavioral sciences. Ph.D. thesis, Committee on Applied Mathematics, Harvard University"},{"key":"4160_CR132","unstructured":"Whitley D, Dominic S, Das R (1991) Genetic reinforcement learning with multi-layer neural networks. In: 4th international conference on genetic algorithms, pp 562\u2013569"},{"key":"4160_CR133","unstructured":"Whitley D, Hanson T (1989) Optimizing neural networks using faster, more accurate genetic search. In: 3rd international conference genetic algorithms, pp 391\u2013396"},{"key":"4160_CR134","doi-asserted-by":"crossref","unstructured":"Xie L, Yuille A (2017) Genetic CNN. In: Proceedings of the 2017 IEEE international conference on computer vision","DOI":"10.1109\/ICCV.2017.154"},{"issue":"4","key":"4160_CR135","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1002\/int.4550080406","volume":"8","author":"X Yao","year":"1993","unstructured":"Yao X (1993) A review of evolutionary artificial neural networks. Int J Intell Syst 8(4):539\u2013567","journal-title":"Int J Intell Syst"},{"issue":"9","key":"4160_CR136","first-page":"1423","volume":"87","author":"X Yao","year":"1999","unstructured":"Yao X (1999) Evolving artificial neural networks. Proc IEEE 87(9):1423\u20131447","journal-title":"Evolving artificial neural networks. Proc IEEE"},{"issue":"3","key":"4160_CR137","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1109\/72.572107","volume":"8","author":"X Yao","year":"1997","unstructured":"Yao X, Liu Y (1997) A new evolutionary system for evolving artificial neural networks. IEEE Trans Neural Netw 8(3):694\u2013713","journal-title":"IEEE Trans Neural Netw"},{"key":"4160_CR138","doi-asserted-by":"crossref","unstructured":"Young SR, Rose DC, Johnston T, Heller WT, Karnowski TP, Potok TE, Patton RM, Perdue G, Miller J (2017) Evolving deep networks using HPC. In: Machine learning on HPC environments workshop, pp 3924\u20133928","DOI":"10.1145\/3146347.3146355"},{"key":"4160_CR139","doi-asserted-by":"crossref","unstructured":"Young SR, Rose DC, Karnowsky TP, Lim SH, Patton RM (2015) Optimizing deep learning hyper-parameters through an evolutionary algorithm. In: Workshop on machine learning in high-performance computing environments","DOI":"10.1145\/2834892.2834896"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-019-04160-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-019-04160-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-019-04160-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,12,4]],"date-time":"2020-12-04T02:37:26Z","timestamp":1607049446000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-019-04160-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,27]]},"references-count":139,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2020,1]]}},"alternative-id":["4160"],"URL":"https:\/\/doi.org\/10.1007\/s00521-019-04160-6","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,3,27]]},"assertion":[{"value":"19 April 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 March 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 March 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}