# Neuroevolution in Deep Neural Networks: Current Trends and Future Challenges

arXiv: https://arxiv.org/abs/2006.05415 · HTML: https://mlsanity.com/paper/2006.05415/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2020.

## Built on by (most cited first, 30 of 33)

- [DECORE: Deep Compression with Reinforcement Learning](https://mlsanity.com/paper/2106.06091/) (2021)
- [Ensemble neuroevolution based approach for multivariate time series anomaly detection](https://mlsanity.com/paper/2108.03585/) (2021)
- [Neuroevolutionary Multi-objective approaches to Trajectory Prediction in Autonomous Vehicles](https://mlsanity.com/paper/2205.02105/) (2022)
- [Surrogate-assisted Multi-objective Neural Architecture Search for Real-time Semantic Segmentation](https://mlsanity.com/paper/2208.06820/) (2022)
- [Advancing CMA-ES with Learning-Based Cooperative Coevolution for Scalable Optimization](https://mlsanity.com/paper/2504.17578/) (2025)
- [FEATHERS: Federated Architecture and Hyperparameter Search](https://mlsanity.com/paper/2206.12342/) (2022)
- [AD-NEV: A Scalable Multi-level Neuroevolution Framework for Multivariate Anomaly Detection](https://mlsanity.com/paper/2305.16497/) (2023)
- [Towards Physical Plausibility in Neuroevolution Systems](https://mlsanity.com/paper/2401.17733/) (2024)
- [Fast and scalable neuroevolution deep learning architecture search for multivariate anomaly detection](https://mlsanity.com/paper/2112.05640/) (2021)
- [On the Evolution of the MCTS Upper Confidence Bounds for Trees by Means of Evolutionary Algorithms in the Game of Carcassonne](https://mlsanity.com/paper/2112.09697/) (2021)
- [Direct evaluation of progression or regression of disease burden in brain metastatic disease with Deep Neuroevolution](https://mlsanity.com/paper/2203.12853/) (2022)
- [NeuroLGP-SM: A Surrogate-assisted Neuroevolution Approach using Linear Genetic Programming](https://mlsanity.com/paper/2403.19459/) (2024)
- [Consistent Relative Confidence and Label-Free Model Selection for Convolutional Neural Networks](https://mlsanity.com/paper/2108.11845/) (2021)
- [Neural Architecture Search Using Genetic Algorithm for Facial Expression Recognition](https://mlsanity.com/paper/2304.12194/) (2023)
- [Evolutionary Algorithms in the Light of SGD: Limit Equivalence, Minima Flatness, and Transfer Learning](https://mlsanity.com/paper/2306.09991/) (2023)
- [Evolving Multi-Objective Neural Network Controllers for Robot Swarms](https://mlsanity.com/paper/2307.14237/) (2023)
- [NeuroLGP-SM: Scalable Surrogate-Assisted Neuroevolution for Deep Neural Networks](https://mlsanity.com/paper/2404.08786/) (2024)
- [Evolutionary Reinforcement Learning via Cooperative Coevolution](https://mlsanity.com/paper/2404.14763/) (2024)
- [A Learning-Based Cooperative Coevolution Framework for Heterogeneous Large-Scale Global Optimization](https://mlsanity.com/paper/2604.01241/) (2026)
- [Neuroevolution deep learning architecture search for estimation of river surface elevation from photogrammetric Digital Surface Models](https://mlsanity.com/paper/2112.12510/) (2021)
- [Evolving Neural Networks with Optimal Balance between Information Flow and Connections Cost](https://mlsanity.com/paper/2202.06163/) (2022)
- [Automatic Synthesis of Neurons for Recurrent Neural Nets](https://mlsanity.com/paper/2207.03577/) (2022)
- [Towards Understanding the Effects of Evolving the MCTS UCT Selection Policy](https://mlsanity.com/paper/2302.03352/) (2023)
- [Sparse Mutation Decompositions: Fine Tuning Deep Neural Networks with Subspace Evolution](https://mlsanity.com/paper/2302.05832/) (2023)
- [Neuroevolutionary algorithms driven by neuron coverage metrics for semi-supervised classification](https://mlsanity.com/paper/2303.02801/) (2023)
- [360^circ High-Resolution Depth Estimation via Uncertainty-aware Structural Knowledge Transfer](https://mlsanity.com/paper/2304.07967/) (2023)
- [A Survey on Multi-Objective Neural Architecture Search](https://mlsanity.com/paper/2307.09099/) (2023)
- [AD-NEv++ : The multi-architecture neuroevolution-based multivariate anomaly detection framework](https://mlsanity.com/paper/2404.07968/) (2024)
- [Interlocking-free Selective Rationalization Through Genetic-based Learning](https://mlsanity.com/paper/2412.10312/) (2024)
- [NeuroPAL: Punctuated Anytime Learning with Neuroevolution for Macromanagement in Starcraft: Brood War](https://mlsanity.com/paper/2506.10384/) (2025)

## Abstract (arXiv)

A variety of methods have been applied to the architectural configuration and learning or training of artificial deep neural networks (DNN). These methods play a crucial role in the success or failure of the DNN for most problems and applications. Evolutionary Algorithms (EAs) are gaining momentum as a computationally feasible method for the automated optimisation and training of DNNs. Neuroevolution is a term which describes these processes of automated configuration and training of DNNs using EAs. While many works exist in the literature, no comprehensive surveys currently exist focusing exclusively on the strengths and limitations of using neuroevolution approaches in DNNs. Prolonged absence of such surveys can lead to a disjointed and fragmented field preventing DNNs researchers potentially adopting neuroevolutionary methods in their own research, resulting in lost opportunities for improving performance and wider application within real-world deep learning problems. This paper presents a comprehensive survey, discussion and evaluation of the state-of-the-art works on using EAs for architectural configuration and training of DNNs. Based on this survey, the paper highlights the most pertinent current issues and challenges in neuroevolution and identifies multiple promising future research directions.