{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T08:32:19Z","timestamp":1775291539131,"version":"3.50.1"},"reference-count":202,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2019,6,13]],"date-time":"2019-06-13T00:00:00Z","timestamp":1560384000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,6,13]],"date-time":"2019-06-13T00:00:00Z","timestamp":1560384000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Rev"],"published-print":{"date-parts":[[2020,3]]},"DOI":"10.1007\/s10462-019-09719-2","type":"journal-article","created":{"date-parts":[[2019,6,13]],"date-time":"2019-06-13T08:05:58Z","timestamp":1560413158000},"page":"1767-1812","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":165,"title":["A survey of swarm and evolutionary computing approaches for deep learning"],"prefix":"10.1007","volume":"53","author":[{"given":"Ashraf","family":"Darwish","sequence":"first","affiliation":[]},{"given":"Aboul Ella","family":"Hassanien","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6843-4508","authenticated-orcid":false,"given":"Swagatam","family":"Das","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,6,13]]},"reference":[{"issue":"1","key":"9719_CR1","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1207\/s15516709cog0901_7","volume":"9","author":"DH Ackley","year":"1985","unstructured":"Ackley DH, Hinton GE, Sejnowski TJ (1985) A learning algorithm for Boltzmann machines. Cognit Sci 9(1):147\u2013169","journal-title":"Cognit Sci"},{"key":"9719_CR2","doi-asserted-by":"crossref","unstructured":"Agapitos A, O\u2019Neill M, Nicolau M, Fagan D, Kattan A, Brabazon A, Curran K (2015) Deep evolution of image representations for handwritten digit recognition. In 2015 IEEE congress on evolutionary computation (CEC). IEEE, pp 2452\u20132459","DOI":"10.1109\/CEC.2015.7257189"},{"key":"9719_CR206","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1016\/j.jpdc.2017.09.006","volume":"117","author":"M Alejandro","year":"2018","unstructured":"Alejandro M, Lara-Cabrera R, Fuentes-Hurtado F, Naranjo V (2018) EvoDeep: A new evolutionary approach for automatic deep neural networks parametrisation. J Parallel Distrib Comput 117:180\u2013191","journal-title":"J Parallel Distrib Comput"},{"key":"9719_CR3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-40137-4","volume-title":"Contemporary evolution strategies","author":"T B\u00e4ck","year":"2013","unstructured":"B\u00e4ck T, Foussette C, Krause P (2013) Contemporary evolution strategies. Springer, Berlin"},{"key":"9719_CR4","doi-asserted-by":"publisher","first-page":"506","DOI":"10.1016\/j.neucom.2017.05.061","volume":"266","author":"H Badem","year":"2017","unstructured":"Badem H, Basturk A, Caliskan A, Yuksel ME (2017) A new efficient training strategy for deep neural networks by hybridization of artificial bee colony and limited-memory BFGS optimization algorithms. Neurocomputing 266:506\u2013526","journal-title":"Neurocomputing"},{"key":"9719_CR5","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1016\/j.eswa.2016.08.027","volume":"64","author":"C Bae","year":"2016","unstructured":"Bae C, Kang K, Liu G, Chung YY (2016) A novel real time video tracking framework using adaptive discrete swarm optimization. Expert Syst Appl 64:385\u2013399","journal-title":"Expert Syst Appl"},{"key":"9719_CR6","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-018-0811-z","author":"A Banharnsakun","year":"2018","unstructured":"Banharnsakun A (2018) Towards improving the convolutional neural networks for deep learning using the distributed artificial bee colony method. Int J Mach Learn Cybern. https:\/\/doi.org\/10.1007\/s13042-018-0811-z","journal-title":"Int J Mach Learn Cybern"},{"key":"9719_CR7","doi-asserted-by":"crossref","unstructured":"Bayer J, Wierstra D, Togelius J, Schmidhuber J (2009) Evolving memory cell structures for sequence learning. In: International conference on artificial neural networks (ICANN 2009), Springer LNCS, pp 755\u2013764","DOI":"10.1007\/978-3-642-04277-5_76"},{"key":"9719_CR8","doi-asserted-by":"crossref","unstructured":"Bengio Y, Lamblin P, Popovici D, Larochelle H (2007) Greedy layer-wise training of deep networks. In: Advances in neural information processing systems, pp 153\u2013160","DOI":"10.7551\/mitpress\/7503.003.0024"},{"issue":"8","key":"9719_CR9","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","volume":"35","author":"Y Bengio","year":"2013","unstructured":"Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798\u20131828","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9719_CR10","doi-asserted-by":"crossref","unstructured":"Biswas A, Chandrakasan AP (2018) Conv-RAM: an energy-efficient SRAM with embedded convolution computation for low-power CNN-based machine learning applications. In: 2018 IEEE international solid-state circuits conference\u2014(ISSCC), San Francisco, CA, pp 488\u2013490","DOI":"10.1109\/ISSCC.2018.8310397"},{"key":"9719_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1162\/EVCO_r_00180","volume":"25","author":"MR Bonyadi","year":"2017","unstructured":"Bonyadi MR, Michalewicz Z (2017) Particle swarm optimization for single objective continuous space problems: a review. Evolut Comput 25:1\u201354","journal-title":"Evolut Comput"},{"key":"9719_CR12","unstructured":"Breuel TM (2015) On the convergence of SGD training of neural networks. arXiv preprint arXiv:1508.02790"},{"key":"9719_CR13","unstructured":"Carreira-Perpinan MA, Hinton GE (2005) On contrastive divergence learning. In: 10th international workshop on artificial intelligence and statistics (AISTATS 2005), pp 59\u201366"},{"issue":"12","key":"9719_CR14","doi-asserted-by":"publisher","first-page":"3123","DOI":"10.1109\/TNNLS.2015.2404823","volume":"26","author":"R Chandra","year":"2015","unstructured":"Chandra R (2015) Competition and collaboration in cooperative coevolution of Elman recurrent neural networks for time-series prediction. IEEE Trans Neural Netw Learn Syst 26(12):3123\u20133136","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"9719_CR15","doi-asserted-by":"publisher","first-page":"514","DOI":"10.1109\/ACCESS.2014.2325029","volume":"2","author":"XW Chen","year":"2014","unstructured":"Chen XW, Lin X (2014) Big data deep learning: challenges and perspectives. IEEE Access 2:514\u2013525","journal-title":"IEEE Access"},{"key":"9719_CR16","doi-asserted-by":"crossref","unstructured":"Chen S, Liu G, Wu C, Jiang Z, Chen J (2016) Image classification with stacked restricted boltzmann machines and evolutionary function array classification voter. In: 2016 IEEE congress on evolutionary computation (CEC). IEEE, pp 4599\u20134606","DOI":"10.1109\/CEC.2016.7744376"},{"key":"9719_CR17","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1016\/j.enconman.2018.03.098","volume":"165","author":"J Chen","year":"2018","unstructured":"Chen J, Zeng GQ, Zhou W, Du W, Lu KD (2018) Wind speed forecasting using nonlinear-learning ensemble of deep learning time series prediction and extremal optimization. Energy Convers Manag 165:681\u2013695","journal-title":"Energy Convers Manag"},{"key":"9719_CR18","doi-asserted-by":"crossref","unstructured":"Cheung B, Sable C (2011) Hybrid evolution of convolutional networks. In: 2011 10th international conference on machine learning and applications workshops. IEEE, pp 293\u2013297","DOI":"10.1109\/ICMLA.2011.73"},{"key":"9719_CR19","doi-asserted-by":"publisher","first-page":"1599","DOI":"10.1007\/978-3-540-92910-9_48","volume-title":"Handbook of natural computing","author":"DW Corne","year":"2012","unstructured":"Corne DW, Reynolds A, Bonabeau E (2012) Swarm intelligence. In: Rozenberg G, B\u00e4ck T, Kok JN (eds) Handbook of natural computing. Springer, Berlin, pp 1599\u20131622"},{"key":"9719_CR20","unstructured":"Das S (2013) Evaluating the evolutionary algorithms\u2014classical perspectives and recent trends, in computational intelligence. In: Ishibuchi H (ed) Encyclopedia of life support systems (EOLSS), Developed under the Auspices of the UNESCO, Eolss Publishers, Oxford, UK. http:\/\/www.eolss.net"},{"key":"9719_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.swevo.2016.01.004","volume":"27","author":"S Das","year":"2016","unstructured":"Das S, Mullick SS, Suganthan PN (2016) Recent advances in differential evolution\u2014an updated survey. Swarm Evolut Comput 27:1\u201330","journal-title":"Swarm Evolut Comput"},{"key":"9719_CR22","doi-asserted-by":"publisher","first-page":"674","DOI":"10.1016\/j.patcog.2018.03.008","volume":"81","author":"S Das","year":"2018","unstructured":"Das S, Datta S, Chaudhuri BB (2018) Handling data irregularities in classification: foundations, trends, and future challenges. Pattern Recognit 81:674\u2013693","journal-title":"Pattern Recognit"},{"issue":"1","key":"9719_CR204","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1007\/s10710-011-9148-z","volume":"13","author":"RW David","year":"2012","unstructured":"David RW (2012) Software review: the ECJ toolkit. Genet Progr Evolvable Mach 13(1):65\u201367","journal-title":"Genet Progr Evolvable Mach"},{"key":"9719_CR23","unstructured":"David OE, Greental I (2014) Genetic algorithms for evolving deep neural networks. In: Proceedings of the companion publication of the 2014 annual conference on genetic and evolutionary computation. ACM, pp 1451\u20131452"},{"key":"9719_CR24","unstructured":"David RC, Precup RE, Petriu EM, Purcaru C, Preitl S (2012) PSO and GSA algorithms for fuzzy controller tuning with reduced process small time constant sensitivity. In: 2012 16th international conference on system theory, control and computing (ICSTCC). IEEE, pp 1\u20136"},{"key":"9719_CR25","first-page":"1","volume":"000","author":"SN Deepa","year":"2017","unstructured":"Deepa SN, Baranilingesan I (2017) Optimized deep learning neural network predictive controller for continuous stirred tank reactor. Comput Electr Eng 000:1\u201316","journal-title":"Comput Electr Eng"},{"key":"9719_CR187","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1016\/j.swevo.2019.04.008","volume":"48","author":"J Del Ser","year":"2019","unstructured":"Del Ser J, Osaba E, Molina D, Yang X-S, Salcedo-Sanz S, Camacho D, Das S, Suganthan PN, Coello Coello CC, Herrera F (2019) Bio-inspired computation: where we stand and what\u2019s next. Swarm Evolut Comput 48:220\u2013250","journal-title":"Swarm Evolut Comput"},{"key":"9719_CR26","doi-asserted-by":"crossref","unstructured":"Desell T (2017) Large scale evolution of convolutional neural networks using volunteer computing. In: Proceedings of the genetic and evolutionary computation conference companion. ACM, pp 127\u2013128","DOI":"10.1145\/3067695.3076002"},{"key":"9719_CR27","doi-asserted-by":"crossref","unstructured":"Desell T, Clachar S, Higgins J, Wild B (2015) Evolving deep recurrent neural networks using ant colony optimization. In: European conference on evolutionary computation in combinatorial optimization. Springer, Cham, pp 86\u201398","DOI":"10.1007\/978-3-319-16468-7_8"},{"key":"9719_CR28","first-page":"2121","volume":"12","author":"J Duchi","year":"2011","unstructured":"Duchi J, Hazan E, Singer Y (2011) Adaptive subgradient methods for online learning and stochastic optimization. J Mach Learn Res 12:2121\u20132159","journal-title":"J Mach Learn Res"},{"key":"9719_CR29","doi-asserted-by":"crossref","unstructured":"Dufourq E, Bassett BA (2017) EDEN: evolutionary deep networks for efficient machine learning. In: Pattern recognition association of South Africa and robotics and mechatronics (PRASA-RobMech). IEEE, pp 110\u2013115","DOI":"10.1109\/RoboMech.2017.8261132"},{"issue":"10","key":"9719_CR30","doi-asserted-by":"publisher","first-page":"760","DOI":"10.1016\/j.advengsoft.2011.05.014","volume":"42","author":"JJ Durillo","year":"2011","unstructured":"Durillo JJ, Nebro AJ (2011) jMetal: a Java framework for multi-objective optimization. Adv Eng Softw 42(10):760\u2013771","journal-title":"Adv Eng Softw"},{"issue":"1","key":"9719_CR31","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.swevo.2011.02.001","volume":"1","author":"AE Eiben","year":"2011","unstructured":"Eiben AE, Smit SK (2011) Parameter tuning for configuring and analyzing evolutionary algorithms. Swarm Evolut Comput 1(1):19\u201331","journal-title":"Swarm Evolut Comput"},{"issue":"2","key":"9719_CR32","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1207\/s15516709cog1402_1","volume":"14","author":"JL Elman","year":"1990","unstructured":"Elman JL (1990) Finding structure in time. Cognit Sci 14(2):179\u2013211","journal-title":"Cognit Sci"},{"key":"9719_CR33","doi-asserted-by":"crossref","unstructured":"ElSaid A, Wild B, Jamiy FE, Higgins J, Desell T (2017) Optimizing LSTM RNNs using ACO to predict turbine engine vibration. In: Proceedings of the genetic and evolutionary computation conference companion. ACM, pp 21\u201322","DOI":"10.1145\/3067695.3082045"},{"key":"9719_CR34","doi-asserted-by":"crossref","unstructured":"ElSaid A, Jamiy FE, Higgins J, Wild B, Desell T (2018) Using ant colony optimization to optimize long short-term memory recurrent neural networks. In: Proceedings of the genetic and evolutionary computation conference. ACM, pp 13\u201320","DOI":"10.1145\/3205455.3205637"},{"issue":"2","key":"9719_CR35","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.advengsoft.2005.04.005","volume":"37","author":"OK Erol","year":"2006","unstructured":"Erol OK, Eksin I (2006) A new optimization method: big bang-big crunch. Adv Eng Softw 37(2):106\u2013111","journal-title":"Adv Eng Softw"},{"key":"9719_CR36","doi-asserted-by":"crossref","unstructured":"Fielding B, Zhang L (2018) Evolving image classification architectures with enhanced particle swarm optimisation. In: IEEE Access, vol 6, pp 68560\u201368575","DOI":"10.1109\/ACCESS.2018.2880416"},{"key":"9719_CR37","doi-asserted-by":"crossref","unstructured":"Fogel DB (1995) Phenotypes, genotypes, and operators in evolutionary computation. In: IEEE international conference on evolutionary computation, 1995, vol 1. IEEE, p 193","DOI":"10.1109\/ICEC.1995.489143"},{"key":"9719_CR38","doi-asserted-by":"crossref","unstructured":"Fujino S, Mori N, Matsumoto K (2017) Deep convolutional networks for human sketches by means of the evolutionary deep learning. In: 2017 Joint 17th world congress of international fuzzy systems association and 9th international conference on soft computing and intelligent systems (IFSA-SCIS). IEEE, pp 1\u20135","DOI":"10.1109\/IFSA-SCIS.2017.8023302"},{"key":"9719_CR39","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/BF00344251","volume":"36","author":"K Fukushima","year":"1980","unstructured":"Fukushima K (1980) Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern 36:193\u2013202","journal-title":"Biol Cybern"},{"key":"9719_CR40","doi-asserted-by":"crossref","unstructured":"Galloway GS, Catterson VM, Fay T, Robb A, Love C (2016) Diagnosis of tidal turbine vibration data through deep neural networks. In: Third European conference of the prognostics and health management society, pp 172\u2013180","DOI":"10.36001\/phme.2016.v3i1.1603"},{"key":"9719_CR41","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.ins.2013.06.017","volume":"247","author":"J Gasc\u00f3n-Moreno","year":"2013","unstructured":"Gasc\u00f3n-Moreno J, Salcedo-Sanz S, Saavedra-Moreno B, Carro-Calvo L, Portilla-Figueras A (2013) An evolutionary-based hyper-heuristic approach for optimal construction of group method of data handling networks. Inf Sci 247:94\u2013108","journal-title":"Inf Sci"},{"key":"9719_CR42","doi-asserted-by":"crossref","unstructured":"Gauci J, Stanley K (2007) Generating large-scale neural networks through discovering geometric regularities. In: Proceedings of the 9th annual conference on genetic and evolutionary computation. ACM, pp 997\u20131004","DOI":"10.1145\/1276958.1277158"},{"issue":"1","key":"9719_CR43","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.media.2015.04.007","volume":"23","author":"R Gauriau","year":"2015","unstructured":"Gauriau R, Cuingnet R, Lesage D, Bloch I (2015) Multi-organ localization with cascaded global-to-local regression and shape prior. Med Image Anal 23(1):70\u201383","journal-title":"Med Image Anal"},{"key":"9719_CR44","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.cogsys.2018.06.001","volume":"52","author":"W Geng","year":"2018","unstructured":"Geng W (2018) Cognitive deep neural networks prediction method for software fault tendency module based on bound particle swarm optimization. Cognit Syst Res 52:12\u201320","journal-title":"Cognit Syst Res"},{"key":"9719_CR45","first-page":"580","volume":"2014","author":"R Girshick","year":"2014","unstructured":"Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. CVPR 2014:580\u2013587","journal-title":"CVPR"},{"key":"9719_CR202","unstructured":"Glorot X, Bordes A, Bengio Y (2011) Deep sparse rectifier networks. In: AISTATS, vol 15, pp 315\u2013323"},{"key":"9719_CR46","unstructured":"Gomes L (2014) Machine-learning maestro michael jordan on the delusions of big data and other huge engineering efforts. In: IEEE spectrum, Oct 20"},{"issue":"12","key":"9719_CR47","doi-asserted-by":"publisher","first-page":"3263","DOI":"10.1109\/TNNLS.2015.2469673","volume":"26","author":"M Gong","year":"2015","unstructured":"Gong M, Liu J, Li H, Cai Q, Su L (2015) A multiobjective sparse feature learning model for deep neural networks. IEEE Trans Neural Netw Learn Syst 26(12):3263\u20133277","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"9719_CR48","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial networks. arXiv:1406.2661"},{"key":"9719_CR49","first-page":"162","volume-title":"Deep learning","author":"I Goodfellow","year":"2015","unstructured":"Goodfellow I, Bengio Y, Courville A (2015) Modern practical deep networks. In: Goodfellow I, Bengio Y, Courville A (eds) Deep learning. MIT Press, Cambridge, pp 162\u2013481"},{"issue":"10","key":"9719_CR50","doi-asserted-by":"publisher","first-page":"2222","DOI":"10.1109\/TNNLS.2016.2582924","volume":"28","author":"K Greff","year":"2017","unstructured":"Greff K, Srivastava RK, Koutn\u00edk J, Steunebrink BR, Schmidhuber J (2017) LSTM: a search space odyssey. IEEE Trans Neural Netw Learn Syst 28(10):2222\u20132232","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"9719_CR51","volume-title":"Principles and techniques of algorithmic differentiation: evaluating derivatives","author":"A Grievank","year":"2000","unstructured":"Grievank A (2000) Principles and techniques of algorithmic differentiation: evaluating derivatives. SIAM, Philadelphia"},{"key":"9719_CR52","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.imu.2018.06.006","volume":"12","author":"S Guo","year":"2018","unstructured":"Guo S, Yang Z (2018) Multi-channel-ResNet: an integration framework towards skin lesion analysis. Inform Med Unlocked 12:67\u201374","journal-title":"Inform Med Unlocked"},{"key":"9719_CR53","unstructured":"Han S, Pool J, Tran J, Dally W (2015) Learning both weights and connections for efficient neural network. In Advances in neural information processing systems, pp 1135\u20131143"},{"key":"9719_CR54","unstructured":"Hardt M, Recht B, Singer Y (2015) Train faster, generalize better: stability of stochastic gradient descent. arXiv preprint arXiv:1509.01240"},{"key":"9719_CR55","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1016\/j.ins.2012.08.023","volume":"222","author":"A Hatamlou","year":"2013","unstructured":"Hatamlou A (2013) Black hole: a new heuristic optimization approach for data clustering. Inf Sci 222:175\u2013184","journal-title":"Inf Sci"},{"key":"9719_CR56","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 (CVPR), Las Vegas, NV, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"issue":"7","key":"9719_CR57","doi-asserted-by":"publisher","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","volume":"18","author":"GE Hinton","year":"2006","unstructured":"Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527\u20131554","journal-title":"Neural Comput"},{"key":"9719_CR58","unstructured":"Hinton GE, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov RR (2012a) Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580"},{"issue":"6","key":"9719_CR59","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1109\/MSP.2012.2205597","volume":"29","author":"G Hinton","year":"2012","unstructured":"Hinton G, Deng L, Yu D, Dahl GE, Mohamed AR, Jaitly N et al (2012b) Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process Mag 29(6):82\u201397","journal-title":"IEEE Signal Process Mag"},{"issue":"8","key":"9719_CR60","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":"9719_CR61","doi-asserted-by":"crossref","unstructured":"Holker G, dos Santos MV (2010) Toward an estimation of distribution algorithm for the evolution of artificial neural networks. In: Proceedings of the third C* conference on computer science and software engineering. ACM, pp 17\u201322","DOI":"10.1145\/1822327.1822330"},{"key":"9719_CR62","doi-asserted-by":"crossref","unstructured":"Horng MH (2017) Fine-tuning parameters of deep belief networks using artificial bee colony algorithm. In: 2017 2nd international conference on artificial intelligence: techniques and applications DEStech transactions on computer science and engineering (AITA 2017)","DOI":"10.12783\/dtcse\/aita2017\/15992"},{"key":"9719_CR63","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Maaten LVD, Weinberger KQ (2017) Densely connected convolutional networks. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), Honolulu, HI, 2017, pp 2261\u20132269","DOI":"10.1109\/CVPR.2017.243"},{"issue":"3","key":"9719_CR64","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1113\/jphysiol.1959.sp006308","volume":"148","author":"DH Hubel","year":"1959","unstructured":"Hubel DH, Wiesel TN (1959) Receptive fields of single neurones in the cat\u2019s striate cortex. J Physiol 148(3):574\u2013591","journal-title":"J Physiol"},{"issue":"1","key":"9719_CR65","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1113\/jphysiol.1968.sp008455","volume":"195","author":"DH Hubel","year":"1968","unstructured":"Hubel DH, Wiesel TN (1968) Receptive fields and functional architecture of monkey striate cortex. J Physiol 195(1):215\u2013243","journal-title":"J Physiol"},{"key":"9719_CR66","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167"},{"key":"9719_CR67","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2018.02.013","author":"M Jain","year":"2018","unstructured":"Jain M, Singh V, Rani A (2018) A novel nature-inspired algorithm for optimization: squirrel search algorithm. Swarm Evolut Comput. https:\/\/doi.org\/10.1016\/j.swevo.2018.02.013","journal-title":"Swarm Evolut Comput"},{"key":"9719_CR68","doi-asserted-by":"publisher","first-page":"628","DOI":"10.1016\/j.ijepes.2013.10.006","volume":"55","author":"S Jiang","year":"2014","unstructured":"Jiang S, Ji Z, Shen Y (2014) A novel hybrid particle swarm optimization and gravitational search algorithm for solving economic emission load dispatch problems with various practical constraints. Int J Electr Power Energy Syst 55:628\u2013644","journal-title":"Int J Electr Power Energy Syst"},{"key":"9719_CR69","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1016\/j.eswa.2017.04.017","volume":"82","author":"S Jiang","year":"2017","unstructured":"Jiang S, Chin KS, Wang L, Qu G, Tsui KL (2017) Modified genetic algorithm-based feature selection combined with pre-trained deep neural network for demand forecasting in outpatient department. Exp Syst Appl 82:216\u2013230","journal-title":"Exp Syst Appl"},{"key":"9719_CR211","doi-asserted-by":"crossref","unstructured":"Junbo T, Weining L, Juneng A, Xueqian W (2015) Fault diagnosis method study in roller bearing based on wavelet transform and stacked auto-encoder. In: The 27th Chinese control and decision conference (2015 CCDC), IEEE 2015, pp 4608\u20134613","DOI":"10.1109\/CCDC.2015.7162738"},{"key":"9719_CR70","doi-asserted-by":"crossref","unstructured":"Justesen N, Risi S (2017) Continual online evolutionary planning for in-game build order adaptation in StarCraft. In: Proceedings of the genetic and evolutionary computation conference. ACM, pp 187\u2013194","DOI":"10.1145\/3071178.3071210"},{"key":"9719_CR71","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1016\/j.asoc.2018.02.037","volume":"66","author":"K Kang","year":"2018","unstructured":"Kang K, Bae C, Yeung HWF, Chung YY (2018) A hybrid gravitational search algorithm with swarm intelligence and deep convolutional feature for object tracking optimization. Appl Soft Comput 66:319\u2013329","journal-title":"Appl Soft Comput"},{"key":"9719_CR72","doi-asserted-by":"crossref","unstructured":"Kenny A, Li X (2017) A study on pre-training deep neural networks using particle swarm optimisation. In: Asia-Pacific conference on simulated evolution and learning. Springer, Cham, pp 361\u2013372","DOI":"10.1007\/978-3-319-68759-9_30"},{"key":"9719_CR73","doi-asserted-by":"crossref","unstructured":"Khalifa MH, Ammar M, Ouarda W, Alimi AM (2017) Particle swarm optimization for deep learning of convolution neural network. In: 2017 Sudan conference on computer science and information technology (SCCSIT). IEEE, pp 1\u20135","DOI":"10.1109\/SCCSIT.2017.8293059"},{"issue":"29","key":"9719_CR74","doi-asserted-by":"publisher","first-page":"e4128","DOI":"10.1002\/cpe.4128","volume":"2017","author":"JK Kim","year":"2017","unstructured":"Kim JK, Han YS, Lee JS (2017) Particle swarm optimization\u2013deep belief network\u2013based rare class prediction model for highly class imbalance problem. Concurr Comput Pract Exp 2017(29):e4128","journal-title":"Concurr Comput Pract Exp"},{"key":"9719_CR75","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980"},{"key":"9719_CR76","unstructured":"Koza JR, Rice JP (1991) Genetic generation of both the weights and architecture for a neural network. In: IJCNN-91-seattle international joint conference on neural networks, vol 2. IEEE, pp 397\u2013404"},{"key":"9719_CR77","doi-asserted-by":"crossref","unstructured":"Kriegman S, Cheney N, Corucci F, Bongard JC (2017) A minimal developmental model can increase evolvability in soft robots. In: Proceedings of the genetic and evolutionary computation conference. ACM, pp 131\u2013138","DOI":"10.1145\/3071178.3071296"},{"key":"9719_CR78","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097\u20131105"},{"key":"9719_CR79","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.neucom.2013.03.047","volume":"137","author":"T Kuremoto","year":"2014","unstructured":"Kuremoto T, Kimura S, Kobayashi K, Obayashi M (2014) Time series forecasting using a deep belief network with restricted Boltzmann machines. Neurocomputing 137:47\u201356","journal-title":"Neurocomputing"},{"key":"9719_CR80","unstructured":"Lamos-Sweeney J, Gaborski R (2012) Deep learning using genetic algorithms. Master thesis, Institute Thomas Golisano College of Computing and Information Sciences. Advisor"},{"key":"9719_CR81","doi-asserted-by":"crossref","unstructured":"Lander S, Shang Y (2015) EvoAE\u2014a new evolutionary method for training autoencoders for deep learning networks. In: 2015 IEEE 39th annual computer software and applications conference (COMPSAC), vol 2. IEEE, pp 790\u2013795","DOI":"10.1109\/COMPSAC.2015.63"},{"key":"9719_CR82","unstructured":"LeCun Y, Boser BE, Denker JS, Henderson D, Howard RE, Hubbard WE, Jackel LD (1990) Handwritten digit recognition with a back-propagation network. In: Advances in neural information processing systems, pp 396\u2013404"},{"issue":"11","key":"9719_CR83","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"},{"issue":"7553","key":"9719_CR84","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\u2013444. https:\/\/doi.org\/10.1038\/nature14539","journal-title":"Nature"},{"key":"9719_CR85","unstructured":"Lee H, Pham P, Largman Y, Ng AY (2009) Unsupervised feature learning for audio classification using convolutional deep belief networks. In: Advances in neural information processing systems, pp 1096\u20131104"},{"key":"9719_CR86","doi-asserted-by":"crossref","unstructured":"Leke C, Ndjiongue AR, Twala B, Marwala T (2017) A deep learning-cuckoo search method for missing data estimation in high-dimensional datasets. In: International conference in swarm intelligence. Springer, Cham, pp 561\u2013572","DOI":"10.1007\/978-3-319-61824-1_61"},{"issue":"1","key":"9719_CR87","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1109\/TNN.2002.804317","volume":"14","author":"FHF Leung","year":"2003","unstructured":"Leung FHF, Lam HK, Ling SH, Tam PKS (2003) Tuning of the structure and parameters of a neural network using an improved genetic algorithm. IEEE Trans Neural Netw 14(1):79\u201388","journal-title":"IEEE Trans Neural Netw"},{"key":"9719_CR88","doi-asserted-by":"crossref","unstructured":"Liang J, Meyerson E, Miikkulainen R (2018) Evolutionary architecture search for deep multitask networks. In: GECCO 18: genetic and evolutionary computation conference, July 15\u201319, Kyoto, Japan. ACM, New York, NY, USA","DOI":"10.1145\/3205455.3205489"},{"key":"9719_CR169","unstructured":"Lieto A, Radicioni DP, Cruciani M (eds) Proceedings of the second international workshop on artificial intelligence and cognition, pp 164\u2013171"},{"issue":"9","key":"9719_CR89","doi-asserted-by":"publisher","first-page":"2553","DOI":"10.1109\/TAC.2015.2394872","volume":"60","author":"Q Liu","year":"2015","unstructured":"Liu Q, Wang Z, He X, Zhou DH (2015a) Event-based H \u221e consensus control of multiagent systems with relative output feedback: the finite-horizon case. IEEE Trans Autom Control 60(9):2553\u20132558","journal-title":"IEEE Trans Autom Control"},{"key":"9719_CR210","doi-asserted-by":"crossref","unstructured":"Liu X, Gao J, He X, Deng L, Duh K, Wang YY (2015b) Representation learning using multi-task deep neural networks for semantic classification and information retrieval. In: Proc. of NAACL, pp 912\u2013921","DOI":"10.3115\/v1\/N15-1092"},{"issue":"12","key":"9719_CR90","doi-asserted-by":"publisher","first-page":"2718","DOI":"10.1109\/TNNLS.2015.2491325","volume":"27","author":"S Liu","year":"2016","unstructured":"Liu S, Hou Z, Yin C (2016) Data-driven modeling for UGI gasification processes via an enhanced genetic BP neural network with link switches. IEEE Trans Neural Netw Learn Syst 27(12):2718\u20132729","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"5","key":"9719_CR91","doi-asserted-by":"publisher","first-page":"1300","DOI":"10.1109\/TSP.2016.2634541","volume":"65","author":"Q Liu","year":"2017","unstructured":"Liu Q, Wang Z, He X, Ghinea G, Alsaadi FE (2017) A resilient approach to distributed filter design for time-varying systems under stochastic nonlinearities and sensor degradation. IEEE Trans Signal Process 65(5):1300\u20131309","journal-title":"IEEE Trans Signal Process"},{"key":"9719_CR92","unstructured":"Liu H, Simonyan K, Vinyals O, Fernando C, Kavukcuoglu K (2018a) Hierarchical representations for efficient architecture search. In: Sixth international conference on learning representations (ICLR 2018). Canada"},{"issue":"6","key":"9719_CR93","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, Wang X, Li H (2018b) Structure learning for deep neural networks based on multiobjective optimization. IEEE Trans Neural Netw Learn Syst 29(6):2450\u20132463","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"9719_CR200","doi-asserted-by":"publisher","first-page":"45","DOI":"10.21037\/mhealth.2017.09.01","volume":"3","author":"B Loh","year":"2017","unstructured":"Loh B, Then P (2017) Deep learning for cardiac computer-aided diagnosis: benefits, issues & solutions. Mhealth 3:45. https:\/\/doi.org\/10.21037\/mhealth.2017.09.01","journal-title":"Mhealth"},{"key":"9719_CR94","doi-asserted-by":"crossref","unstructured":"L\u00f3pez-Ib\u00e1\u00f1ez M, St\u00fctzle T, Dorigo M (2018) Ant colony optimization: a component-wise overview. In: Handbook of heuristics, pp 371\u2013407","DOI":"10.1007\/978-3-319-07124-4_21"},{"key":"9719_CR95","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.asoc.2017.12.036","volume":"65","author":"A Lopez-Rincon","year":"2018","unstructured":"Lopez-Rincon A, Tonda A, Elati M, Schwander O, Piwowarski B, Gallinari P (2018) Evolutionary optimization of convolutional neural networks for cancer miRNA biomarkers classification. Appl Soft Comput 65:91\u2013100","journal-title":"Appl Soft Comput"},{"key":"9719_CR96","unstructured":"Lorenzo PR, Nalepa J (2018) Memetic evolution of deep neural networks. In: Proceedings of the genetic and evolutionary computation conference. ACM, pp 505\u2013512"},{"key":"9719_CR97","doi-asserted-by":"crossref","unstructured":"Lorenzo PR, Nalepa J, Kawulok M, Ramos LS, Pastor JR (2017) Particle swarm optimization for hyper-parameter selection in deep neural networks. In: Proceedings of the genetic and evolutionary computation conference. ACM, pp 481\u2013488","DOI":"10.1145\/3071178.3071208"},{"key":"9719_CR98","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1016\/j.sigpro.2016.07.028","volume":"130","author":"C Lu","year":"2017","unstructured":"Lu C, Wang ZY, Qin WL, Ma J (2017) Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification. Signal Process 130:377\u2013388","journal-title":"Signal Process"},{"issue":"7","key":"9719_CR99","doi-asserted-by":"publisher","first-page":"3524","DOI":"10.1109\/TAC.2016.2614486","volume":"62","author":"L Ma","year":"2017","unstructured":"Ma L, Wang Z, Lam HK (2017a) Event-triggered mean-square consensus control for time-varying stochastic multi-agent system with sensor saturations. IEEE Trans Autom Control 62(7):3524\u20133531","journal-title":"IEEE Trans Autom Control"},{"issue":"7","key":"9719_CR100","doi-asserted-by":"publisher","first-page":"1050","DOI":"10.1109\/TSMC.2016.2531657","volume":"47","author":"L Ma","year":"2017","unstructured":"Ma L, Wang Z, Lam HK (2017b) Mean-square H\u221e consensus control for a class of nonlinear time-varying stochastic multiagent systems: the finite-horizon case. IEEE Trans Syst Man Cybern Syst 47(7):1050\u20131060","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"issue":"1\u20134","key":"9719_CR101","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/S0925-2312(01)00596-3","volume":"42","author":"M Mandischer","year":"2002","unstructured":"Mandischer M (2002) A comparison of evolution strategies and backpropagation for neural network training. Neurocomputing 42(1\u20134):87\u2013117","journal-title":"Neurocomputing"},{"key":"9719_CR102","unstructured":"Mandt S, Hoffman M, Blei D (2016) A variational analysis of stochastic gradient algorithms. In: International conference on machine learning, pp 354\u2013363"},{"issue":"4\u20136","key":"9719_CR103","doi-asserted-by":"publisher","first-page":"887","DOI":"10.1016\/j.neucom.2008.04.058","volume":"72","author":"D Maravall","year":"2009","unstructured":"Maravall D, de Lope J (2009) Hybridizing evolutionary computation and reinforcement learning for the design of almost universal controllers for autonomous robots. Neurocomputing 72(4\u20136):887\u2013894","journal-title":"Neurocomputing"},{"key":"9719_CR104","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1016\/j.jpdc.2017.09.006","volume":"117","author":"A Martin","year":"2018","unstructured":"Martin A, Lara-Cabrera R, Fuentes-Hurtado F, Naranjo V, Camacho D (2018) EvoDeep: a new evolutionary approach for automatic deep neural networks parametrisation. J Parallel Distrib Comput 117:180\u2013191","journal-title":"J Parallel Distrib Comput"},{"issue":"4","key":"9719_CR105","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/BF02478259","volume":"5","author":"WS McCulloch","year":"1943","unstructured":"McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5(4):115\u2013133","journal-title":"Bull Math Biophys"},{"key":"9719_CR106","doi-asserted-by":"crossref","unstructured":"Miikkulainen R (2017) Neuroevolution. In: Encyclopedia of machine learning and data mining, pp 899\u2013904","DOI":"10.1007\/978-1-4899-7687-1_594"},{"key":"9719_CR107","unstructured":"Miikkulainen R et al (2017) Evolving deep neural networks. arXiv preprint arXiv:1703.00548"},{"key":"9719_CR108","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.advengsoft.2016.01.008","volume":"95","author":"S Mirjalili","year":"2016","unstructured":"Mirjalili S, Andrew L (2016) The whale optimization algorithm. Adv Eng Softw 95:51\u201367","journal-title":"Adv Eng Softw"},{"key":"9719_CR109","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","volume":"69","author":"S Mirjalili","year":"2014","unstructured":"Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46\u201361","journal-title":"Adv Eng Softw"},{"issue":"4","key":"9719_CR110","doi-asserted-by":"publisher","first-page":"61:1","DOI":"10.1145\/2742642","volume":"47","author":"A Mukhopadhyay","year":"2015","unstructured":"Mukhopadhyay A, Maulik U, Bandyopadhyay S (2015) A survey of multiobjective evolutionary clustering. ACM Comput Surv 47(4):61:1\u201361:46","journal-title":"ACM Comput Surv"},{"key":"9719_CR111","unstructured":"Nair V, Hinton GE (2010) Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th international conference on machine learning (ICML-10), pp 807\u2013814"},{"key":"9719_CR112","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.swevo.2011.11.003","volume":"2","author":"F Neri","year":"2012","unstructured":"Neri F, Cotta C (2012) Memetic algorithms and memetic computing optimization: a literature review. Swarm Evolut Comput 2:1\u201314","journal-title":"Swarm Evolut Comput"},{"key":"9719_CR113","unstructured":"Neyshabur B, Salakhutdinov RR, Srebro N (2015) Path-sgd: path-normalized optimization in deep neural networks. In: Advances in neural information processing systems, pp 2422\u20132430"},{"key":"9719_CR114","doi-asserted-by":"publisher","first-page":"875","DOI":"10.1016\/j.asoc.2015.08.043","volume":"46","author":"JP Papa","year":"2016","unstructured":"Papa JP, Scheirer W, Cox DD (2016) Fine-tuning deep belief networks using harmony search. Appl Soft Comput 46:875\u2013885","journal-title":"Appl Soft Comput"},{"key":"9719_CR115","doi-asserted-by":"crossref","unstructured":"Parker A, Nitschke G (2017) Autonomous intersection driving with neuro-evolution. In: Proceedings of the genetic and evolutionary computation conference companion. ACM, pp 133\u2013134","DOI":"10.1145\/3067695.3076012"},{"issue":"3","key":"9719_CR116","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1109\/MCS.2002.1004010","volume":"22","author":"KM Passino","year":"2002","unstructured":"Passino KM (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst 22(3):52\u201367","journal-title":"IEEE Control Syst"},{"key":"9719_CR117","doi-asserted-by":"crossref","unstructured":"Passos LA, Rodrigues DR, Papa JP (2018) Fine tuning deep boltzmann machines through meta-heuristic approaches. In: 2018 IEEE 12th international symposium on applied computational intelligence and informatics (SACI). IEEE, pp 000419\u2013000424","DOI":"10.1109\/SACI.2018.8440959"},{"key":"9719_CR118","doi-asserted-by":"crossref","unstructured":"Pawe\u0142czyk K, Kawulok M, Nalepa J (2018) Genetically-trained deep neural networks. In: Proceedings of the genetic and evolutionary computation conference companion. ACM, pp 63\u201364","DOI":"10.1145\/3205651.3208763"},{"issue":"1","key":"9719_CR119","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/S0933-3657(99)00047-0","volume":"19","author":"CA Pe\u00f1a-Reyes","year":"2000","unstructured":"Pe\u00f1a-Reyes CA, Sipper M (2000) Evolutionary computation in medicine: an overview. Artif Intell Med 19(1):1\u201323","journal-title":"Artif Intell Med"},{"issue":"2018","key":"9719_CR207","doi-asserted-by":"publisher","first-page":"1301","DOI":"10.1016\/j.energy.2018.05.052","volume":"162","author":"L Peng","year":"2018","unstructured":"Peng L, Liu S, Liu R, Wang L (2018) Effective long short-term memory with differential evolution algorithm for electricity price prediction. Energy 162(2018):1301\u20131314","journal-title":"Energy"},{"key":"9719_CR120","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1016\/j.asoc.2014.03.039","volume":"21","author":"AP Piotrowski","year":"2014","unstructured":"Piotrowski AP (2014) Differential evolution algorithms applied to neural network training suffer from stagnation. Appl Soft Comput 21:382\u2013406","journal-title":"Appl Soft Comput"},{"key":"9719_CR121","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.swevo.2016.06.001","volume":"32","author":"A Rajasekhar","year":"2017","unstructured":"Rajasekhar A, Lynn N, Das S, Suganthan PN (2017) Computing with the collective intelligence of honey bees\u2013a survey. Swarm Evolut Comput 32:25\u201348","journal-title":"Swarm Evolut Comput"},{"issue":"3","key":"9719_CR122","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/j.cad.2010.12.015","volume":"43","author":"RV Rao","year":"2011","unstructured":"Rao RV, Savsani VJ, Vakharia DP (2011) Teaching\u2013learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303\u2013315","journal-title":"Comput Aided Des"},{"issue":"13","key":"9719_CR123","doi-asserted-by":"publisher","first-page":"2232","DOI":"10.1016\/j.ins.2009.03.004","volume":"179","author":"E Rashedi","year":"2009","unstructured":"Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232\u20132248","journal-title":"Inf Sci"},{"key":"#cr-split#-9719_CR124.1","doi-asserted-by":"crossref","unstructured":"Rawal A, Miikkulainen R (2016) Evolving deep LSTM-based memory networks using an information maximization objective. In: Friedrich T","DOI":"10.1145\/2908812.2908941"},{"key":"#cr-split#-9719_CR124.2","unstructured":"(ed) Proceedings of the genetic and evolutionary computation conference 2016 (GECCO'16). ACM, New York, NY, USA, pp 501-508"},{"key":"9719_CR125","first-page":"2902","volume":"2017","author":"E Real","year":"2017","unstructured":"Real E, Moore S, Selle A, Saxena S, Suematsu YL, Tan J, Le QV, Kurakin A (2017) Large-scale evolution of image classifiers. ICML 2017:2902\u20132911","journal-title":"ICML"},{"key":"9719_CR126","unstructured":"Real E, Aggarwal A, Huang Y, Le QV (2018) Regularized evolution for image classifier architecture search. arXiv preprint arXiv:1802.01548"},{"key":"9719_CR127","doi-asserted-by":"crossref","unstructured":"Reddy KK, Sarkar S, Venugopalan V, Giering M (2016) Anomaly detection and fault disambiguation in large flight data: a multi-modal deep auto-encoder approach. In: Annual conference of the prognostics and health management society, Denver, Colorado, pp 1\u20138","DOI":"10.36001\/phmconf.2016.v8i1.2549"},{"key":"9719_CR205","doi-asserted-by":"crossref","unstructured":"Risi S, Stanley KO (2012) A unified approach to evolving plasticity and neural geometry. In: International joint conference on neural networks. IEEE, pp 1\u20138","DOI":"10.1109\/IJCNN.2012.6252826"},{"key":"9719_CR128","doi-asserted-by":"crossref","unstructured":"Rosa G, Papa J, Marana A, Scheirer W, Cox D (2015) Fine-tuning convolutional neural networks using harmony search. In: Iberoamerican congress on pattern recognition. Springer, Cham, pp 683\u2013690","DOI":"10.1007\/978-3-319-25751-8_82"},{"key":"9719_CR129","doi-asserted-by":"crossref","unstructured":"Rosa G, Papa J, Costa K, Passos L, Pereira C, Yang XS (2016) Learning parameters in deep belief networks through firefly algorithm. In: IAPR workshop on artificial neural networks in pattern recognition. Springer, Cham, pp 138\u2013149","DOI":"10.1007\/978-3-319-46182-3_12"},{"key":"9719_CR201","unstructured":"Salakhutdinov R, Hinton GE (2009) Deep Boltzmann machines. In: AISTATS: 1, p 3"},{"key":"9719_CR130","unstructured":"Salakhutdinov R, Larochelle H (2010) Efficient learning of deep Boltzmann machines. In: Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp 693\u2013700"},{"key":"9719_CR131","unstructured":"Salimans T, Ho J, Chen X, Sidor S, Sutskever I (2017) Evolution strategies as a scalable alternative to reinforcement learning. arXiv:1703.03864"},{"key":"9719_CR132","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2017\/4180510","volume":"2017","author":"D S\u00e1nchez","year":"2017","unstructured":"S\u00e1nchez D, Melin P, Castillo O (2017) A grey Wolf optimizer for modular granular neural networks for human recognition. Comput Intell Neurosci 2017:1\u201326","journal-title":"Comput Intell Neurosci"},{"issue":"4","key":"9719_CR133","doi-asserted-by":"publisher","first-page":"778","DOI":"10.1109\/TASLP.2014.2303296","volume":"22","author":"R Sarikaya","year":"2014","unstructured":"Sarikaya R, Hinton GE, Deoras A (2014) Application of deep belief networks for natural language understanding. IEEE\/ACM Trans Audio Speech Lang Process (TASLP) 22(4):778\u2013784","journal-title":"IEEE\/ACM Trans Audio Speech Lang Process (TASLP)"},{"key":"9719_CR134","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","volume":"61","author":"J Schmidhuber","year":"2015","unstructured":"Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85\u2013117","journal-title":"Neural Netw"},{"key":"9719_CR208","unstructured":"Shafiee M, Wong A (2016) Evolutionary synthesis of deep neural networks via synaptic cluster-driven genetic encoding. In: NIPS Workshop on efficient methods for deep neural networks. Thirtieth conference on neural information processing systems, Barcelona, Spain, Dec 5\u201310, 2016"},{"key":"9719_CR135","doi-asserted-by":"crossref","unstructured":"Shenfield A, Rostami S (2017) Multi-objective evolution of artificial neural networks in multi-class medical diagnosis problems with class imbalance. In: 2017 IEEE conference on computational intelligence in bioinformatics and computational biology (CIBCB). IEEE, pp 1\u20138","DOI":"10.1109\/CIBCB.2017.8058553"},{"issue":"4","key":"9719_CR136","doi-asserted-by":"publisher","first-page":"35","DOI":"10.4018\/ijsir.2011100103","volume":"2","author":"Y Shi","year":"2011","unstructured":"Shi Y (2011) An optimization algorithm based on brainstorming process. Int J Swarm Intell Res 2(4):35\u201362","journal-title":"Int J Swarm Intell Res"},{"key":"9719_CR137","doi-asserted-by":"publisher","unstructured":"Shinozaki T, Watanabe S (2015) Structure discovery of deep neural network based on evolutionary algorithms. In: 2015 IEEE international conference on acoustics, speech, and signal processing, ICASSP 2015\u2014proceedings, vol 2015-August, [7178918] Institute of Electrical and Electronics Engineers Inc., pp 4979\u20134983. https:\/\/doi.org\/10.1109\/icassp.2015.7178918","DOI":"10.1109\/icassp.2015.7178918"},{"issue":"7587","key":"9719_CR138","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, Guez A, Sifre L, Van Den Driessche G (2016) Mastering the game of Go with deep neural networks and tree search. Nature 529(7587):484\u2013489","journal-title":"Nature"},{"key":"9719_CR139","volume-title":"Evolutionary optimization algorithms","author":"D Simon","year":"2013","unstructured":"Simon D (2013) Evolutionary optimization algorithms. Wiley, New York"},{"key":"9719_CR140","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: ICLR"},{"key":"9719_CR209","doi-asserted-by":"crossref","unstructured":"Singh P, Dwivedi P (2018) Integration of new evolutionary approach with artificial neural network for solving short term load forecast problem. In: Applied energy, vol 217(C). Elsevier, pp 537\u2013549","DOI":"10.1016\/j.apenergy.2018.02.131"},{"key":"9719_CR141","doi-asserted-by":"publisher","first-page":"444","DOI":"10.1016\/j.neucom.2015.06.054","volume":"171","author":"J Song","year":"2016","unstructured":"Song J, Niu Y (2016) Resilient finite-time stabilization of fuzzy stochastic systems with randomly occurring uncertainties and randomly occurring gain fluctuations. Neurocomputing 171:444\u2013451","journal-title":"Neurocomputing"},{"key":"9719_CR212","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1016\/j.neucom.2016.06.016","volume":"214","author":"YS Song","year":"2016","unstructured":"Song YS, Hu J, Chen D, Ji D, Liu F (2016) Recursive approach to networked fault estimation with packet dropouts and randomly occurring uncertainties. Neurocomputing 214:340\u2013349","journal-title":"Neurocomputing"},{"issue":"1","key":"9719_CR142","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014a) 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":"1","key":"9719_CR143","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014b) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929\u20131958","journal-title":"J Mach Learn Res"},{"key":"9719_CR144","unstructured":"Srivastava RK, Greff K, Schmidhuber J (2015) Highway networks. arXiv:1505.00387"},{"issue":"2","key":"9719_CR145","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":"9719_CR146","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, Miikkulainen R (2019) Designing neural networks through neuroevolution. Nat Mach Intell 1:24\u201335","journal-title":"Nat Mach Intell"},{"key":"9719_CR147","doi-asserted-by":"publisher","first-page":"1078","DOI":"10.1016\/j.asoc.2018.07.013","volume":"70","author":"PN Suganthan","year":"2018","unstructured":"Suganthan PN (2018) On non-iterative learning algorithms with closed-form solution. Appl Soft Comput 70:1078\u20131082","journal-title":"Appl Soft Comput"},{"key":"9719_CR148","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2018.2881143","author":"Y Sun","year":"2018","unstructured":"Sun Y, Xue B, Zhang M, Yen GG (2018a) A particle swarm optimization-based flexible convolutional autoencoder for image classification. IEEE Trans Neural Netw Learn Syst. https:\/\/doi.org\/10.1109\/tnnls.2018.2881143","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"9719_CR149","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1109\/TEVC.2018.2808689","volume":"23","author":"Y Sun","year":"2018","unstructured":"Sun Y, Yen GG, Yi Z (2018b) Evolving unsupervised deep neural networks for learning meaningful representations. IEEE Trans Evolut Comput 23:89\u2013103","journal-title":"IEEE Trans Evolut Comput"},{"key":"9719_CR150","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: 2015 IEEE conference on computer vision and pattern recognition (CVPR), Boston, MA, pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"9719_CR190","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.neunet.2018.01.016","volume":"101","author":"T Takase","year":"2018","unstructured":"Takase T, Oyama S, Kurihara M (2018) Effective neural network training with adaptive learning rate based on training loss. Neural Netw 101:68\u201378","journal-title":"Neural Netw"},{"key":"9719_CR152","doi-asserted-by":"crossref","unstructured":"Tan Y, Zhu Y (2010) Fireworks algorithm for optimization. In: International conference in swarm intelligence. Springer, Berlin, pp 355\u2013364","DOI":"10.1007\/978-3-642-13495-1_44"},{"issue":"5","key":"9719_CR153","doi-asserted-by":"publisher","first-page":"933","DOI":"10.1109\/TNNLS.2014.2329097","volume":"26","author":"SC Tan","year":"2015","unstructured":"Tan SC, Watada J, Ibrahim Z, Khalid M (2015) Evolutionary fuzzy ARTMAP neural networks for classification of semiconductor defects. IEEE Trans Neural Netw Learn Syst 26(5):933\u2013950","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"9719_CR154","unstructured":"Team TTD, Al-Rfou R, Alain G, Almahairi A, Angermueller C, Bahdanau D et al (2016) Theano: a python framework for fast computation of mathematical expressions. arXiv preprint arXiv:1605.02688"},{"key":"9719_CR155","doi-asserted-by":"crossref","unstructured":"Thirukovalluru R, Dixit S, Sevakula RK, Verma NK, Salour A (2016) Generating feature sets for fault diagnosis using denoising stacked auto-encoder. In: 2016 IEEE international conference on prognostics and health management (ICPHM). IEEE, pp 1\u20137","DOI":"10.1109\/ICPHM.2016.7542865"},{"key":"9719_CR156","unstructured":"Tieleman T, Hinton GE (2012) Lecture 6.5\u2014rmsprop, COURSERA: neural networks for machine learning"},{"key":"9719_CR157","unstructured":"Tirumala SS (2014) Implementation of evolutionary algorithms for deep architectures. CEUR workshop proceedings"},{"key":"9719_CR203","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.neunet.2018.01.016","volume":"101","author":"T Tomoumi","year":"2018","unstructured":"Tomoumi T, Satoshi O, Masahito K (2018) Effective neural network training with adaptive learning rate based on training loss. Neural Netw 101:68\u201378","journal-title":"Neural Netw"},{"issue":"3","key":"9719_CR158","first-page":"440","volume":"21","author":"A Trivedi","year":"2017","unstructured":"Trivedi A, Srinivasan D, Sanyal K, Ghosh A (2017) A survey of multiobjective evolutionary algorithms based on decomposition. IEEE Trans Evolut Comput 21(3):440\u2013462","journal-title":"IEEE Trans Evolut Comput"},{"key":"9719_CR159","first-page":"3371","volume":"11","author":"P Vincent","year":"2010","unstructured":"Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol PA (2010) Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11:3371\u20133408","journal-title":"J Mach Learn Res"},{"key":"9719_CR160","unstructured":"Wan L, Zeiler M, Zhang S, Le Cun Y, Fergus R (2013) Regularization of neural networks using dropconnect. In: International conference on machine learning, pp 1058\u20131066"},{"issue":"6","key":"9719_CR161","doi-asserted-by":"publisher","first-page":"1331","DOI":"10.1109\/TNNLS.2016.2536104","volume":"28","author":"B Wang","year":"2017","unstructured":"Wang B, Merrick KE, Abbass HA (2017) Co-operative coevolutionary neural networks for mining functional association rules. IEEE Trans Neural Netw Learn Syst 28(6):1331\u20131344","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"9719_CR162","doi-asserted-by":"crossref","unstructured":"Wang B, Sun Y, Xue B, Zhang M (2018a) A hybrid differential evolution approach to designing deep convolutional neural networks for image classification. In: The Australasian joint conference on artificial intelligence (AI 2018). Springer, pp 237\u2013250","DOI":"10.1007\/978-3-030-03991-2_24"},{"key":"9719_CR163","doi-asserted-by":"crossref","unstructured":"Wang B, Sun Y, Xue B, Zhang M (2018b) Evolving deep convolutional neural networks by variable-length particle swarm optimization for image classification. arXiv preprint arXiv:1803.06492","DOI":"10.1109\/CEC.2018.8477735"},{"key":"9719_CR164","doi-asserted-by":"crossref","unstructured":"Wang R, Clune J, Stanley KO (2018c) VINE: an open source interactive data visualization tool for neuroevolution. In: GECCO \u201818 companion: genetic and evolutionary computation conference companion, July 15\u201319, Kyoto, Japan. ACM, New York, NY, USA","DOI":"10.1145\/3205651.3208236"},{"key":"9719_CR165","doi-asserted-by":"publisher","DOI":"10.1109\/tevc.2019.2895748","author":"C Wang","year":"2019","unstructured":"Wang C, Xu C, Yao X, Tao D (2019) Evolutionary generative adversarial networks. IEEE Trans Evolut Comput. https:\/\/doi.org\/10.1109\/tevc.2019.2895748","journal-title":"IEEE Trans Evolut Comput"},{"key":"9719_CR166","doi-asserted-by":"crossref","unstructured":"Wiatowski T, B\u00f6lcskei H (2018) A mathematical theory of deep convolutional neural networks for feature extraction. In: IEEE transactions on information theory, vol 64(3), pp 1845\u20131866","DOI":"10.1109\/TIT.2017.2776228"},{"key":"9719_CR167","unstructured":"Wu ZY, Rahaman A (2017) Optimized deep learning framework for water distribution data-driven modeling. In: XVIII international conference on water distribution systems analysis, WDSA2016, Procedia Engineering, vol 186, pp 261\u2013268"},{"key":"9719_CR168","doi-asserted-by":"crossref","unstructured":"Xie L, Yuille A (2017) Genetic CNN. In: 2017 IEEE international conference on computer vision (ICCV), Venice, pp 1388\u20131397","DOI":"10.1109\/ICCV.2017.154"},{"key":"9719_CR170","volume-title":"Nature-inspired metaheuristic algorithms","author":"XS Yang","year":"2010","unstructured":"Yang XS (2010) Nature-inspired metaheuristic algorithms, 2nd edn. Luniver Press, Frome","edition":"2"},{"issue":"Part A","key":"9719_CR171","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1016\/j.neucom.2015.10.071","volume":"175","author":"H Yang","year":"2016","unstructured":"Yang H, Wang Z, Shu H, Alsaadi FE, Hayat T (2016) Almost sure H\u221e sliding mode control for nonlinear stochastic systems with Markovian switching and time-delays. Neurocomputing 175(Part A):392\u2013400","journal-title":"Neurocomputing"},{"issue":"9","key":"9719_CR172","doi-asserted-by":"publisher","first-page":"1423","DOI":"10.1109\/5.784219","volume":"87","author":"X Yao","year":"1999","unstructured":"Yao X (1999) Evolving artificial neural networks. Proc IEEE 87(9):1423\u20131447","journal-title":"Proc IEEE"},{"issue":"3","key":"9719_CR173","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 Learn Syst 8(3):694\u2013713","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"12","key":"9719_CR174","doi-asserted-by":"publisher","first-page":"e0188746","DOI":"10.1371\/journal.pone.0188746","volume":"12","author":"F Ye","year":"2017","unstructured":"Ye F (2017) Particle swarm optimization-based automatic parameter selection for deep neural networks and its applications in large-scale and high-dimensional data. PLoS ONE 12(12):e0188746","journal-title":"PLoS ONE"},{"issue":"2","key":"9719_CR175","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1049\/iet-cta.2013.0854","volume":"9","author":"Y Yuan","year":"2014","unstructured":"Yuan Y, Sun F, Liu H, Yang H (2014a) Low-frequency robust control for singularly perturbed system. IET Control Theory Appl 9(2):203\u2013210","journal-title":"IET Control Theory Appl"},{"key":"9719_CR176","doi-asserted-by":"crossref","unstructured":"Yuan Z, Lu Y, Wang Z, Xue Y (2014b) Droid-sec: deep learning in android malware detection. In: ACM SIGCOMM computer communication review, vol 44(4). ACM., pp 371\u2013372","DOI":"10.1145\/2740070.2631434"},{"issue":"1","key":"9719_CR177","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1109\/TST.2016.7399288","volume":"21","author":"Z Yuan","year":"2016","unstructured":"Yuan Z, Lu Y, Xue Y (2016) Droiddetector: android malware characterization and detection using deep learning. Tsinghua Sci Technol 21(1):114\u2013123","journal-title":"Tsinghua Sci Technol"},{"key":"9719_CR178","doi-asserted-by":"crossref","unstructured":"Zagoruyko S, Komodakis N (2016) Wide residual networks. arXiv preprint arXiv:1605.07146","DOI":"10.5244\/C.30.87"},{"issue":"10","key":"9719_CR179","doi-asserted-by":"publisher","first-page":"2306","DOI":"10.1109\/TNNLS.2016.2582798","volume":"28","author":"C Zhang","year":"2017","unstructured":"Zhang C, Lim P, Qin AK, Tan KC (2017a) Multiobjective deep belief networks ensemble for remaining useful life estimation in prognostics. IEEE Trans Neural Netw Learn Syst 28(10):2306\u20132318","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"9719_CR180","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2018.2832648","author":"C Zhang","year":"2017","unstructured":"Zhang C, Tan KC, Li H, Hong GS (2017b) A cost-sensitive deep belief network for imbalanced classification. IEEE Trans Neural Netw Learn Syst. https:\/\/doi.org\/10.1109\/tnnls.2018.2832648","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"#cr-split#-9719_CR181.1","unstructured":"Zhong Z, Yan J, Liu C-L (2018) Practical network blocks design with q-learning. In"},{"key":"#cr-split#-9719_CR181.2","unstructured":"Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR 2018), pp 2423-2432"},{"key":"9719_CR182","doi-asserted-by":"crossref","unstructured":"Zhou C, Paffenroth RC (2017) Anomaly detection with robust deep autoencoders. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 665\u2013674","DOI":"10.1145\/3097983.3098052"},{"key":"9719_CR183","doi-asserted-by":"crossref","unstructured":"Zhou S, Chen Q, Wang X (2010) Discriminative deep belief networks for image classification. In 2010 17th IEEE international conference on image processing (ICIP). IEEE, pp 1561\u20131564","DOI":"10.1109\/ICIP.2010.5649922"},{"issue":"1","key":"9719_CR184","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.swevo.2011.03.001","volume":"1","author":"A Zhou","year":"2011","unstructured":"Zhou A, Qu BY, Li H, Zhao SZ, Suganthan PN, Zhang Q (2011) Multiobjective evolutionary algorithms: a survey of the state of the art. Swarm Evolut Comput 1(1):32\u201349","journal-title":"Swarm Evolut Comput"},{"issue":"1","key":"9719_CR185","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.artmed.2014.04.001","volume":"61","author":"G Zhu","year":"2014","unstructured":"Zhu G, Lizotte D, Hoey J (2014) Scalable approximate policies for Markov decision process models of hospital elective admissions. Artif Intell Med 61(1):21\u201334","journal-title":"Artif Intell Med"},{"key":"9719_CR186","unstructured":"Zoph B, Vasudevan V, Shlens J, Le QV (2017) Learning transferable architectures for scalable image recognition. arXiv preprint arXiv:1707.07012"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-019-09719-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10462-019-09719-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-019-09719-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,19]],"date-time":"2024-07-19T17:30:35Z","timestamp":1721410235000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10462-019-09719-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,13]]},"references-count":202,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2020,3]]}},"alternative-id":["9719"],"URL":"https:\/\/doi.org\/10.1007\/s10462-019-09719-2","relation":{},"ISSN":["0269-2821","1573-7462"],"issn-type":[{"value":"0269-2821","type":"print"},{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,6,13]]},"assertion":[{"value":"13 June 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}