Data to 7 Oct 2026

Evolving Deep Neural Networks

arXiv 1703.00548 ↗First posted 1 Mar 2017cs.NE cs.AIcited by 93

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Abstract (arXiv)

The success of deep learning depends on finding an architecture to fit the task. As deep learning has scaled up to more challenging tasks, the architectures have become difficult to design by hand. This paper proposes an automated method, CoDeepNEAT, for optimizing deep learning architectures through evolution. By extending existing neuroevolution methods to topology, components, and hyperparameters, this method achieves results comparable to best human designs in standard benchmarks in object recognition and language modeling. It also supports building a real-world application of automated image captioning on a magazine website. Given the anticipated increases in available computing power, evolution of deep networks is promising approach to constructing deep learning applications in the future.

Key facts. Cited by 93 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Mar 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 33; 2022: 14; 2023: 16; 2024: 16; 2025: 11; 2026: 3.

93 papers.

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2021Symbolic Regression via Neural-Guided Genetic Programming Population Seedingcited by 97
2021Partial Is Better Than All: Revisiting Fine-tuning Strategy for Few-shot Learningcited by 72
2021AlphaEvolve: A Learning Framework to Discover Novel Alphas in Quantitative Investmentcited by 31
2021Towards Improving the Consistency, Efficiency, and Flexibility of Differentiable Neural Architecture Searchcited by 27
2022Generalizing Few-Shot NAS with Gradient Matchingcited by 25
2024SLED: Self Logits Evolution Decoding for Improving Factuality in Large Language Modelscited by 16
2022Computer Vision and Deep Learning for Fish Classification in Underwater Habitats: A Surveycited by 14
2024SoK: Dataset Copyright Auditing in Machine Learning Systemscited by 14
2021Memory-Efficient Differentiable Transformer Architecture Searchcited by 13
2023Search to Fine-tune Pre-trained Graph Neural Networks for Graph-level Taskscited by 13
2024GI-NAS: Boosting Gradient Inversion Attacks Through Adaptive Neural Architecture Searchcited by 13
2022A Survey on Computationally Efficient Neural Architecture Searchcited by 12

38 more

2024Neural Exploratory Landscape Analysis for Meta-Black-Box-Optimizationcited by 10
2021RANK-NOSH: Efficient Predictor-Based Architecture Search via Non-Uniform Successive Halvingcited by 9
2022Automated Reinforcement Learning: An Overviewcited by 9
2022Construction of Hierarchical Neural Architecture Search Spaces based on Context-free Grammarscited by 9
2023Dynamic Neural Network for Multi-Task Learning Searching across Diverse Network Topologiescited by 9
2024Growing Tiny Networks: Spotting Expressivity Bottlenecks and Fixing Them Optimallycited by 9
2021Ensemble neuroevolution based approach for multivariate time series anomaly detectioncited by 8
2023Feature Programming for Multivariate Time Series Predictioncited by 7
2021Neural Architecture Search via Bregman Iterationscited by 5
2021AutoInit: Analytic Signal-Preserving Weight Initialization for Neural Networkscited by 5
2024einspace: Searching for Neural Architectures from Fundamental Operationscited by 5
2021Visionary: Vision architecture discovery for robot learningcited by 4
2021MONCAE: Multi-Objective Neuroevolution of Convolutional Autoencoderscited by 4
2021CONetV2: Efficient Auto-Channel Size Optimization for CNNscited by 4
2021Fitness Landscape Footprint: A Framework to Compare Neural Architecture Search Problemscited by 4
2022Evolving Neural Selection with Adaptive Regularizationcited by 4
2023Multiobjective Evolutionary Pruning of Deep Neural Networks with Transfer Learning for improving their Performance and Robustnesscited by 4
2024Directed Structural Adaptation to Overcome Statistical Conflicts and Enable Continual Learningcited by 4
2021Evolving GAN Formulations for Higher Quality Image Synthesiscited by 3
2021Efficient Sparse Artificial Neural Networkscited by 3
2021MANAS: Multi-Scale and Multi-Level Neural Architecture Search for Low-Dose CT Denoisingcited by 3
2023Divide-and-Conquer the NAS puzzle in Resource Constrained Federated Learning Systemscited by 3
2023AD-NEV: A Scalable Multi-level Neuroevolution Framework for Multivariate Anomaly Detectioncited by 3
2023The Deep Arbitrary Polynomial Chaos Neural Network or how Deep Artificial Neural Networks could benefit from Data-Driven Homogeneous Chaos Theorycited by 3
2021Evolutionary Generative Adversarial Networks with Crossover Based Knowledge Distillationcited by 2
2021A Novel Framework for Neural Architecture Search in the Hill Climbing Domaincited by 2
2021Network Architecture Search for Face Enhancementcited by 2
2021An Analysis of Super-Net Heuristics in Weight-Sharing NAScited by 2
2021A Data-driven Approach to Neural Architecture Search Initializationcited by 2
2021Fast and scalable neuroevolution deep learning architecture search for multivariate anomaly detectioncited by 2
2022An Adaptive and Stability-Promoting Layerwise Training Approach for Sparse Deep Neural Network Architecturecited by 2
2023Adaptive Ensemble Learning: Boosting Model Performance through Intelligent Feature Fusion in Deep Neural Networkscited by 2
2023A Survey on Multi-Objective based Parameter Optimization for Deep Learningcited by 2
2023Optimizing Neural Networks with Gradient Lexicase Selectioncited by 2
2024NeuroLGP-SM: A Surrogate-assisted Neuroevolution Approach using Linear Genetic Programmingcited by 2
2024Flexible ViG: Learning the Self-Saliency for Flexible Object Recognitioncited by 2
2025Topological derivative approach for deep neural network architecture adaptationcited by 2
2021Efficient Evolutionary Models with Digraphonscited by 1

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