This page collects the evolutionary algorithms for deep learning

Please email me ysun@scu.edu.cn if you find new or mistakes for these information

  1. Stepwise PathNet: a layer-by-layer knowledge-selection-based transfer learning algorithm [2020, Imai et al.] [Scientific Reports]
  2. A particle swarm optimization-based flexible convolutional auto-encoder for image classification [2018, Sun et al.][IEEE Transactions on Neural Networks and Learning Systems, DOI:10.1109/TNNLS.2018.2881143.]
  3. Evolving unsupervised deep neural networks for learning meaningful representations [2018, Sun et al.] [IEEE Transactions on Evolutionary Computation]
  4. Automatically Designing CNN Architectures Using the Genetic Algorithm for Image Classification [2020, Sun et al.] [IEEE Transactions on Cybernetics]
  5. Evolving deep convolutional neural networks for image classification [2020, Sun et al.] [IEEE Transactions on Evolutionary Computation]
  6. An experimental study on hyper-parameter optimization for stacked auto-encoders [2018, Sun et al.] [2018 IEEE Congress on Evolutionary Computation (CEC)]
  7. Completely Automated CNN Architecture Design Based on Blocks [2020, Sun et al.] [IEEE transactions on neural networks and learning systems]
  8. Surrogate-assisted evolutionary deep learning using an end-to-end random forest-based performance predictor [2020, Sun et al.] [IEEE Transactions on Evolutionary Computation]
  9. A Hybrid GA-PSO Method for Evolving Architecture and Short Connections of Deep Convolutional Neural Networks [2019, Wang et al.] [Springer PRICAI 2019: Trends in Artificial Intelligence]
  10. A Hybrid Differential Evolution Approach to Designing Deep Convolutional Neural Networks for Image Classification [2018, Wang et al.] [Springer AI 2018: Advances in Artificial Intelligence]
  11. Evolving deep convolutional neural networks by variable-length particle swarm optimization for image classification [2018, Wang et al.] [CEC 2018]
  12. Evolving deep neural networks by multi-objective particle swarm optimization for image classification [2019, Wang et al.] [Proceedings of the Genetic and Evolutionary Computation Conference]
  13. Evolving deep convolutional neural networks for hyperspectral image denoising [2020, Liu et al.] [IJCNN 2020]
  14. Hierarchical representations for efficient architecture search [2018, Liu et al.] [ICLR2018]
  15. Genetic CNN [2017, Xie et al.] [ICCV 2017]
  16. A genetic programming approach to designing convolutional neural network architectures [2017, Suganuma et al.] [GECCO 2017]
  17. Large-scale evolution of image classifiers [2017, Real et al.] [ICML2017]
  18. A hypercube-based encoding for evolving large-scale neural networks [2009, Stanley et al.] [MIT Press Artificial life]
  19. Structure discovery of deep neural network based on evolutionary algorithms [2015, Shinozaki et al.] [2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)]
  20. Genetic deep neural networks using different activation functions for financial data mining [2015, Zhang et al.] [2015 IEEE International Conference on Big Data (Big Data)]
  21. Automated structure discovery and parameter tuning of neural network language model based on evolution strategy [2016, Tanaka et al.] [2016 IEEE Spoken Language Technology Workshop (SLT)]
  22. Boosting Neuro Evolutionary Techniques for Speech Recognition [2019, Anwar et al.] [2019 International Conference on Electrical, Computer and Communication Engineering (ECCE)]
  23. An Evolutionary Approach to Compact DAG Neural Network Optimization [2019, Chiu et al.] [IEEE Access]
  24. Evolutionary Design of Deep Neural Networks [2019, Radu et al.] [2019 21st International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)]
  25. Evolving deep neural networks using coevolutionary algorithms with multi-population strategy [2020, Tirumala et al.] [Springer Neural Computing and Applications]
  26. Hybrid evolution of convolutional networks [2011, Cheung et al.] [2011 10th International Conference on Machine Learning and Applications and Workshops]
  27. Structure learning for deep neural networks based on multiobjective optimization [2017, Liu et al.] [IEEE transactions on neural networks and learning systems]
  28. Evolution-based configuration optimization of a Deep Neural Network for the classification of Obstructive Sleep Apnea episodes [2019, Falco et al.] [Elsevier Future Generation Computer Systems]
  29. A Multi-objective Particle Swarm Optimization for Neural Networks Pruning [2019, Wu et al.] [2019 IEEE Congress on Evolutionary Computation (CEC)]
  30. Effective long short-term memory with differential evolution algorithm for electricity price prediction [2018, Peng et al.] [Elsevier Energy]
  31. Evolving memory cell structures for sequence learning [2009, Bayer et al.] [Springer International Conference on Artificial Neural Networks]
  32. Learning Structural Similarity with Evolutionary-GAN: A New Face De-identification Method [2019, Song et al.] [IEEE 2019 6th International Conference on Behavioral, Economic and Socio-Cultural Computing (BESC)]
  33. Particle swarm optimization-based automatic parameter selection for deep neural networks and its applications in large-scale and high-dimensional data [2017, Ye et al.] [Public Library of Science PloS one]
  34. Evolving ensemble models for image segmentation using enhanced particle swarm optimization [2019, Tan et al.] [IEEE access]
  35. Day-ahead traffic flow forecasting based on a deep belief network optimized by the multi-objective particle swarm algorithm [2019, Li et al.] [Elsevier Knowledge-Based Systems]
  36. Differential evolution optimization for resilient stacked sparse autoencoder and its applications on bearing fault diagnosis [2018, Saufi et al.] [IOP Publishing Measurement Science and Technology]
  37. A GPSO-optimized convolutional neural networks for EEG-based emotion recognition [2020, Gao et al.] [Elsevier Neurocomputing]
  38. Particle swarm optimization--deep belief network--based rare class prediction model for highly class imbalance problem [2017, Kim et al.] [Wiley Online Library Concurrency and Computation: Practice and Experience]
  39. Ant-based Neural Topology Search (ANTS) for Optimizing Recurrent Networks [2020, ElSaid et al.] [Springer International Conference on the Applications of Evolutionary Computation (Part of EvoStar)]
  40. NPENAS: Neural Predictor Guided Evolution for Neural Architecture Search [2020, Wei et al.] [arXiv]
  41. AutoML-Zero: Evolving Machine Learning Algorithms From Scratch [2020, Real et al.] [arXiv]
  42. Automatically Searching for U-Net Image Translator Architecture [2020, Shu et al.] [arXiv]
  43. Searching for accurate binary neural architectures [2019, Shen et al.] [Proceedings of the IEEE International Conference on Computer Vision Workshops]
  44. Efficient Evolutionary Architecture Search for CNN Optimization on GTSRB [2019, Johner et al.] [2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)]
  45. Neural Architecture Search for Skin Lesion Classification [2020, Kwasigroch et al.] [IEEE Access]
  46. Neural Architecture Search for Deep Image Prior [2020, Ho et al.] [arXiv]
  47. DeepMaker: A Multi-Objective Optimization Framework for Deep Neural Networks in Embedded Systems [2020, Loni et al.] [Elsevier Microprocessors and Microsystems]
  48. Automated Neural Network Construction with Similarity Sensitive Evolutionary Algorithms [2019, Tian et al.] [2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI)]
  49. A Genetic Algorithm based Kernel-size Selection Approach for a Multi-column Convolutional Neural Network [2019, Singh et al.] [arXiv]
  50. Neural architecture search for image saliency fusion [2020, Bianco et al.] [Elsevier Information Fusion]
  51. Artificial Neural Network and Accelerator Co-design using Evolutionary Algorithms [2019, Colangelo et al.] [2019 IEEE High Performance Extreme Computing Conference (HPEC)]
  52. ImmuNeCS: Neural Committee Search by an Artificial Immune System [2019, Frachon et al.] [arXiv]
  53. ImmuNetNAS: An Immune-network approach for searching Convolutional Neural Network Architectures [2020, Chen et al.] [arXiv]
  54. Enhancing Neural Architecture Search with Speciation and Inter-Epoch Crossover [2019, Baughman et al.]
  55. Deep neural network architecture search using network morphism [2019, Kwasigroch et al.] [IEEE 2019 24th International Conference on Methods and Models in Automation and Robotics (MMAR)]
  56. A Novel Automatic CNN Architecture Design Approach Based on Genetic Algorithm [2019, Ahmed et al.] [Springer International Conference on Advanced Intelligent Systems and Informatics]
  57. Automated design of error-resilient and hardware-efficient deep neural networks [2019, Schorn et al.] [arXiv]
  58. Genetic Neural Architecture Search for automatic assessment of human sperm images [2019, Miahi et al.] [arXiv]
  59. Cars: Continuous evolution for efficient neural architecture search [2019, Yang et al.] [arXiv]
  60. Compute-Efficient Neural Network Architecture Optimization by a Genetic Algorithm [2019, Litzinger et al.] [Springer International Conference on Artificial Neural Networks]
  61. Self-adaptive 2D-3D ensemble of fully convolutional networks for medical image segmentation [2020, Calisto et al.] [International Society for Optics and Photonics Medical Imaging 2020: Image Processing]
  62. Neural network architecture search with differentiable cartesian genetic programming for regression [2019, M{\"a}rtens et al.] [Proceedings of the Genetic and Evolutionary Computation Conference Companion]
  63. Fairnas: Rethinking evaluation fairness of weight sharing neural architecture search [2019, Chu et al.] [arXiv]
  64. Evolving Robust Neural Architectures to Defend from Adversarial Attacks [2019, Vargas et al.] [arXiv]
  65. Adaptive Genomic Evolution of Neural Network Topologies (AGENT) for State-to-Action Mapping in Autonomous Agents [2019, Behjat et al.] [IEEE 2019 International Conference on Robotics and Automation (ICRA)]
  66. Automatic Modulation Recognition Using Neural Architecture Search [2019, Wei et al.] [IEEE 2019 International Conference on High Performance Big Data and Intelligent Systems (HPBD\&IS)]
  67. StyleNAS: An Empirical Study of Neural Architecture Search to Uncover Surprisingly Fast End-to-End Universal Style Transfer Networks [2019, An et al.] [arXiv]
  68. EENA: efficient evolution of neural architecture [2019, Zhu et al.] [Proceedings of the IEEE International Conference on Computer Vision Workshops]
  69. Regularized Evolutionary Algorithm for Dynamic Neural Topology Search [2019, Saltori et al.] [Springer International Conference on Image Analysis and Processing]
  70. Automatic Design of Artificial Neural Networks for Gamma-Ray Detection [2019, Assun{\c{c}}{\~a}o et al.] [IEEE Access]
  71. AdaResU-Net: Multiobjective adaptive convolutional neural network for medical image segmentation [2019, Baldeon-Calisto et al.] [Elsevier Neurocomputing]
  72. Fast denser: Efficient deep neuroevolution [2019, Assun{\c{c}}{\~a}o et al.] [Springer European Conference on Genetic Programming]
  73. Incremental Evolution and Development of Deep Artificial Neural Networks [2020, Assun{\c{c}}ao et al.] [Springer Genetic Programming: 23rd European Conference, EuroGP 2020, Held as Part of EvoStar 2020, Seville, Spain, April 15--17, 2020, Proceedings 23]
  74. Size/Accuracy Trade-Off in Convolutional Neural Networks: An Evolutionary Approach [2019, Cetto et al.] [Springer INNS Big Data and Deep Learning conference]
  75. Evolution of Deep Convolutional Neural Networks Using Cartesian Genetic Programming [2020, Suganuma et al.] [MIT Press Evolutionary Computation]
  76. Deep Evolutionary Networks with Expedited Genetic Algorithms for Medical Image Denoising [2019, Liu et al.] [Elsevier Medical image analysis]
  77. Evolutionary Cell Aided Design for Neural Network Architectures [2019, Colangelo et al.] [arXiv]
  78. Evolutionary neural automl for deep learning [2019, Liang et al.] [Proceedings of the Genetic and Evolutionary Computation Conference]
  79. GeneCAI: Genetic Evolution for Acquiring Compact AI [2020, Javaheripi et al.] [arXiv]
  80. Investigating recurrent neural network memory structures using neuro-evolution [2019, Ororbia et al.] [Proceedings of the Genetic and Evolutionary Computation Conference]
  81. Fast, accurate and lightweight super-resolution with neural architecture search [2019, Chu et al.] [arXiv]
  82. Joint neural architecture search and quantization [2018, Chen et al.] [arXiv]
  83. Gradient based evolution to optimize the structure of convolutional neural networks [2018, Mitschke et al.] [2018 25th IEEE International Conference on Image Processing (ICIP)]
  84. Lamarckian evolution of convolutional neural networks [2018, Prellberg et al.] [Springer International Conference on Parallel Problem Solving from Nature]
  85. Optimizing deep learning hyper-parameters through an evolutionary algorithm [2015, Young et al.] [Proceedings of the Workshop on Machine Learning in High-Performance Computing Environments]
  86. CMA-ES for hyperparameter optimization of deep neural networks [2016, Loshchilov et al.] [arXiv]
  87. Neuroevolution: from architectures to learning [2008, Floreano et al.] [Springer Evolutionary intelligence]
  88. An evolutionary algorithm that constructs recurrent neural networks [1994, Angeline et al.] [IEEE transactions on Neural Networks]
  89. Evolving deep neural networks [2019, Miikkulainen et al.] [Elsevier Artificial Intelligence in the Age of Neural Networks and Brain Computing]
  90. Coronavirus Optimization Algorithm: A bioinspired metaheuristic based on the COVID-19 propagation model [2020, Mart{\'\i}nez-{\'A}lvarez et al.] [arXiv]
  91. Evolving recurrent neural networks for time series data prediction of coal plant parameters [2019, ElSaid et al.] [Springer International Conference on the Applications of Evolutionary Computation (Part of EvoStar)]
  92. Using ant colony optimization to optimize long short-term memory recurrent neural networks [2018, ElSaid et al.] [Proceedings of the Genetic and Evolutionary Computation Conference]
  93. Optimizing long short-term memory recurrent neural networks using ant colony optimization to predict turbine engine vibration [2018, ElSaid et al.] [Elsevier Applied Soft Computing]
  94. Evolving deep recurrent neural networks using ant colony optimization [2015 Desell et al.] [Springer European Conference on Evolutionary Computation in Combinatorial Optimization]
  95. Spatial evolutionary generative adversarial networks [2019, Toutouh et al.] [Proceedings of the Genetic and Evolutionary Computation Conference]
  96. Differential-Evolution-Based Generative Adversarial Networks for Edge Detection [2019, Zheng et al.] [Proceedings of the IEEE International Conference on Computer Vision Workshops]
  97. Evolutionary generative adversarial networks [2019, Wang et al.] [IEEE Transactions on Evolutionary Computation]
  98. Effective Mutation and Recombination for Evolving Convolutional Networks [2020, Dahal et al.] [Proceedings of the 3rd International Conference on Applications of Intelligent Systems]
  99. A novel channel pruning method for deep neural network compression [2018, Hu et al.] [arXiv]
  100. Evolutionary Multi-Objective Optimization Driven by Generative Adversarial Networks (GANs) [2019, He et al.] [arXiv]
  101. Deep neural networks compression learning based on multiobjective evolutionary algorithms [2020, Huang et al.] [Elsevier Neurocomputing]
  102. Multiobjective deep belief networks ensemble for remaining useful life estimation in prognostics [2016, Zhang et al.] [IEEE transactions on neural networks and learning systems]
  103. Deep learning using genetic algorithms [2012, Lamos-Sweeney et al.]
  104. Evolution strategy based neural network optimization and LSTM language model for robust speech recognition [2016, Tanaka et al.] [Cit. on]
  105. Multiobjective evolution of deep learning parameters for robot manipulator object recognition and grasping [2018, Hossain et al.] [Taylor \& Francis Advanced Robotics]
  106. Designing neural networks using genetic algorithms with graph generation system [1990, Kitano et al.] [Complex systems]
  107. Designing Neural Networks using Genetic Algorithms [1989, Miller et al.] [ICGA]
  108. Arabic sentiment classification using convolutional neural network and differential evolution algorithm [2019, Dahou et al.] [Hindawi Computational intelligence and neuroscience]
  109. Particle swarm optimisation for evolving artificial neural network [2000, Zhang et al.] [Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics.'cybernetics evolving to systems, humans, organizations, and their complex interactions'(cat. no. 0)]
  110. Evolving autoencoding structures through genetic programming [2019, Rodriguez-Coayahuitl et al.] [Springer Genetic Programming and Evolvable Machines]
  111. Evolving Efficient Deep Neural Networks for Real-time Object Recognition [2019, Lan et al.] [2019 IEEE Symposium Series on Computational Intelligence (SSCI)]
  112. Development of an Evolutionary Deep Neural Net for Materials Research [2020, Roy et al.] [Springer TMS 2020 149th Annual Meeting \& Exhibition Supplemental Proceedings]
  113. Emotion Estimation by Joint Facial Expression and Speech Tonality Using Evolutionary Deep Learning Structures [2019, Chung et al.] [2019 IEEE 8th Global Conference on Consumer Electronics (GCCE)]
  114. An Evolutionary Deep Learning Approach Using Genetic Programming with Convolution Operators for Image Classification [2019, Bi et al.] [2019 IEEE Congress on Evolutionary Computation (CEC)]
  115. Evolutionary deep learning: A genetic programming approach to image classification [2018, Evans et al.] [2018 IEEE Congress on Evolutionary Computation (CEC)]
  116. Recognizing the order of four-scene comics by evolutionary deep learning [2018, Fujino et al.] [Springer International Symposium on Distributed Computing and Artificial Intelligence]
  117. The evolutionary deep learning based on deep convolutional neural network for the anime storyboard recognition [2017, Fujino et al.] [Springer International Symposium on Distributed Computing and Artificial Intelligence]
  118. Deep convolutional networks for human sketches by means of the evolutionary deep learning [2017, Fujino et al.] [IEEE 2017 Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS)]
  119. Evolutionary LSTM-FCN networks for pattern classification in industrial processes [2020, Ortego et al.] [Elsevier Swarm and Evolutionary Computation]
  120. An evolutionary hyper-heuristic to optimise deep belief networks for image reconstruction [2019, Sabar et al.] [Elsevier Applied Soft Computing]
  121. An Automated Approach for Developing a Convolutional Neural Network Using a Modified Firefly Algorithm for Image Classification [2020, Sharaf et al.] [Springer Applications of Firefly Algorithm and its Variants]
  122. An Evolutionary Optimization Algorithm for Gradually Saturating Objective Functions [2020, Sapra et al.]
  123. Constrained Evolutionary Piecemeal Training to Design Convolutional Neural Networks [2020, Sapra et al.] [International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems. Springer]
  124. Auto-creation of Effective Neural Network Architecture by Evolutionary Algorithm and ResNet for Image Classification [2019, Chen et al.] [2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)]
  125. PSO-based optimized CNN for Hindi ASR [2019, Passricha et al.] [Springer International Journal of Speech Technology]
  126. Identify Hierarchical Structures from Task-Based fMRI Data via Hybrid Spatiotemporal Neural Architecture Search Net [2019, Zhang et al.] [Springer International Conference on Medical Image Computing and Computer-Assisted Intervention]
  127. Efficient network architecture search via multiobjective particle swarm optimization based on decomposition [2020, Jiang et al.] [Elsevier Neural Networks]
  128. Neural Architecture Search for Optimizing Deep Belief Network Models of fMRI Data [2019, Qiang et al.] [Springer International Workshop on Multiscale Multimodal Medical Imaging]
  129. An Evolutionary Approach to Variational Autoencoders [2020, Hajewski et al.] [IEEE 2020 10th Annual Computing and Communication Workshop and Conference (CCWC)]
  130. Efficient Neural Network Space with Genetic Search [2019, Kang et al.] [Springer International Conference on Bio-Inspired Computing: Theories and Applications]
  131. Regularized evolution for image classifier architecture search [2019, Real et al.] [Proceedings of the aaai conference on artificial intelligence]
  132. Reinforced evolutionary neural architecture search [2018, Chen et al.] [arXiv]
  133. Nsga-net: A multi-objective genetic algorithm for neural architecture search [2018, Lu et al.]
  134. Evolutionary-neural hybrid agents for architecture search [2018, Maziarz et al.] [arXiv]
  135. Evolutionary Neural Architecture Search for Image Restoration [2019, van et al.] [IEEE 2019 International Joint Conference on Neural Networks (IJCNN)]
  136. NSGA-Net: neural architecture search using multi-objective genetic algorithm [2019, Lu et al.] [Proceedings of the Genetic and Evolutionary Computation Conference]
  137. Multi-objective reinforced evolution in mobile neural architecture search [2019, Chu et al.] [arXiv]
  138. Real-time Federated Evolutionary Neural Architecture Search [2020, Zhu et al.] [arXiv]
  139. Evolutionary Neural Architecture Search for Retinal Vessel Segmentation [2020, Fan et al.] [arXiv]
  140. Sampled Training and Node Inheritance for Fast Evolutionary Neural Architecture Search [2020, Zhang et al.] [arXiv]
  141. Evolutionary neural architecture search for deep learning [2019, Liang et al.]
  142. Searching toward pareto-optimal device-aware neural architectures [2018, Cheng et al.] [Proceedings of the International Conference on Computer-Aided Design]
  143. Evolving Knowledge And Structure Through Evolution-based Neural Architecture Search [2019, Wang et al.] [NTNU]
  144. Multi-objective evolutionary federated learning [2019, Zhu et al.] [IEEE transactions on neural networks and learning systems]
  145. Particle swarm optimization of deep neural networks architectures for image classification [2019, Junior et al.] [Elsevier Swarm and Evolutionary Computation]
  146. Particle Swarm Optimisation for Evolving Deep Neural Networks for Image Classification by Evolving and Stacking Transferable Blocks [2019, Wang et al.] [arXiv]
  147. EEGNAS: Neural Architecture Search for Electroencephalography Data Analysis and Decoding [2019, Rapaport et al.] [Springer International Workshop on Human Brain and Artificial Intelligence]
  148. Catalytic thermal degradation of Chlorella vulgaris: Evolving deep neural networks for optimization [2019, Teng et al.] [Elsevier Bioresource technology]
  149. Evolving image classification architectures with enhanced particle swarm optimisation [2018, Fielding et al.] [IEEE Access]
  150. Designing compact convolutional neural network for embedded stereo vision systems [2018, Loni et al.] [2018 IEEE 12th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC)]
  151. Memetic evolution of deep neural networks [2018, Lorenzo et al.] [Proceedings of the Genetic and Evolutionary Computation Conference]
  152. A New Compensatory Genetic Algorithm-Based Method for Effective Compressed Multi-function Convolutional Neural Network Model Selection with Multi-Objective Optimization [2019, Zhang et al.] [arXiv]
  153. Automatic Model Selection for Neural Networks [2019, Laredo et al.] [arXiv]
  154. Deepswarm: Optimising convolutional neural networks using swarm intelligence [2019, Byla et al.] [Springer UK Workshop on Computational Intelligence]
  155. The Ant Swarm Neuro-Evolution Procedure for Optimizing Recurrent Networks [2019, ElSaid et al.] [arXiv]
  156. Exploiting the potential of standard convolutional autoencoders for image restoration by evolutionary search [2018, Suganuma et al.] [arXiv]
  157. From nodes to networks: Evolving recurrent neural networks [2018, Rawal et al.] [arXiv]
  158. EIGEN: Ecologically-Inspired GENetic Approach for Neural Network Structure Searching from Scratch [2019, Ren et al.] [Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition]
  159. Evolutionary architecture search for deep multitask networks [2018, Liang et al.] [Proceedings of the Genetic and Evolutionary Computation Conference]
  160. Single path one-shot neural architecture search with uniform sampling [2019, Guo et al.] [arXiv]
  161. The evolved transformer [2019, So et al.] [arXiv]
  162. A Graph-Based Encoding for Evolutionary Convolutional Neural Network Architecture Design [2019, Irwin-Harris et al.] [2019 IEEE Congress on Evolutionary Computation (CEC)]
  163. Evolutionary stochastic gradient descent for optimization of deep neural networks [2018, Cui et al.] [Advances in neural information processing systems]
  164. Multi-Criterion Evolutionary Design of Deep Convolutional Neural Networks [2019, Lu et al.] [arXiv]
  165. Efficient residual dense block search for image super-resolution [2019, Song et al.] [arXiv]
  166. DarwinML: A Graph-based Evolutionary Algorithm for Automated Machine Learning [2018, Qi et al.] [arXiv]
  167. A specialized evolutionary strategy using mean absolute error random sampling to design recurrent neural networks [2019, Camero et al.] [arXiv]
  168. Evolution of convolutional highway networks [2018, Kramer et al.] [Springer International Conference on the Applications of Evolutionary Computation]
  169. Evolving space-time neural architectures for videos [2019, Piergiovanni et al.] [Proceedings of the IEEE International Conference on Computer Vision]
  170. Evolutionary deep learning for car park occupancy prediction in smart cities [2018, Camero et al.] [Springer International Conference on Learning and Intelligent Optimization]
  171. Evolutionary deep learning-based energy consumption prediction for buildings [2018, Almalaq et al.] [IEEE Access]
  172. Automated problem identification: Regression vs classification via evolutionary deep networks [2017, Dufourq et al.] [Proceedings of the South African Institute of Computer Scientists and Information Technologists]
  173. EvoDeep: a new evolutionary approach for automatic deep neural networks parametrisation [2018, Mart{\'\i}n et al.] [Elsevier Journal of Parallel and Distributed Computing]
  174. An Evolutionary Deep Learning Method for Short-term Wind Speed Prediction: A Case Study of the Lillgrund Offshore Wind Farm [2020, Neshat et al.] [arXiv]
  175. Evolutionary Deep Learning to Identify Galaxies in the Zone of Avoidance [2019, Jones et al.] [arXiv]
  176. EvoU-Net: an evolutionary deep fully convolutional neural network for medical image segmentation [2020, Hassanzadeh et al.] [Proceedings of the 35th Annual ACM Symposium on Applied Computing]
  177. Evolutionary Deep Learning Approaches for Financial Prediction [2019, 정혜정 et al.] [이화여자대학교 대학원]
  178. Waste generation prediction under uncertainty in smart cities through deep neuroevolution [2019, Camero et al.] [Universidad de Antioquia Revista Facultad de Ingenier{\'\i}a Universidad de Antioquia]
  179. A genetic algorithm for convolutional network structure optimization for concrete crack detection [2018, Gibb et al.] [2018 IEEE Congress on Evolutionary Computation (CEC)]
  180. Genetic Programming and Gradient Descent: A Memetic Approach to Binary Image Classification [2019, Evans et al.] [arXiv]
  181. Efficient multi-objective neural architecture search via lamarckian evolution [2018, Elsken et al.] [arXiv]
  182. Simple and efficient architecture search for convolutional neural networks [2017, Elsken et al.] [arXiv]
  183. Deep learning architecture search by neuro-cell-based evolution with function-preserving mutations [2018, Wistuba et al.] [Springer Joint European Conference on Machine Learning and Knowledge Discovery in Databases]
  184. TPOT: A Tree-based Pipeline Optimization Tool for Automating Machine Learning [2016, Olson et al.] [Workshop on Automatic Machine Learning]
  185. Particle swarm optimization for hyper-parameter selection in deep neural networks [2017, Lorenzo et al.] [Proceedings of the genetic and evolutionary computation conference]
  186. Evolving artificial neural networks [1999, Yao et al.] [Proceedings of the IEEE]
  187. Evolving neural networks through augmenting topologies [2002, Stanley et al.] [MIT Press Evolutionary computation]
  188. Efficient Evolutionary Deep Neural Architecture Search (NAS) by Noisy Network Morphism Mutation [2019, Chen et al.] [Springer International Conference on Bio-Inspired Computing: Theories and Applications]