# Survey on Evolutionary Deep Learning: Principles, Algorithms, Applications and Open Issues

arXiv: https://arxiv.org/abs/2208.10658 · HTML: https://mlsanity.com/paper/2208.10658/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Aug 2022.

## Built on by (most cited first, 12 of 12)

- [Evolution of Heuristics: Towards Efficient Automatic Algorithm Design Using Large Language Model](https://mlsanity.com/paper/2401.02051/) (2024)
- [Algorithm Evolution Using Large Language Model](https://mlsanity.com/paper/2311.15249/) (2023)
- [AutoSAT: Automatically Optimize SAT Solvers via Large Language Models](https://mlsanity.com/paper/2402.10705/) (2024)
- [Learn from Global Correlations: Enhancing Evolutionary Algorithm via Spectral GNN](https://mlsanity.com/paper/2412.17629/) (2024)
- [An algorithmic framework for the optimization of deep neural networks architectures and hyperparameters](https://mlsanity.com/paper/2303.12797/) (2023)
- [EvoMAS: Evolutionary Generation of Multi-Agent Systems](https://mlsanity.com/paper/2602.06511/) (2026)
- [GBO:AMulti-Granularity Optimization Algorithm via Granular-ball for Continuous Problems](https://mlsanity.com/paper/2303.12807/) (2023)
- [Humans Coexist, So Must Embodied Artificial Agents](https://mlsanity.com/paper/2502.04809/) (2025)
- [Evolutionary Computation for the Design and Enrichment of General-Purpose Artificial Intelligence Systems: Survey and Prospects](https://mlsanity.com/paper/2407.08745/) (2024)
- [ABG-NAS: Adaptive Bayesian Genetic Neural Architecture Search for Graph Representation Learning](https://mlsanity.com/paper/2504.21254/) (2025)
- [StereoFactory: A Unified Merging Framework for Robust Stereo Matching](https://mlsanity.com/paper/2606.17475/) (2026)
- [Uncertainty-Driven Replay Memory for Reinforcement Learning](https://mlsanity.com/paper/2608.29860/) (2026)

## Abstract (arXiv)

Over recent years, there has been a rapid development of deep learning (DL) in both industry and academia fields. However, finding the optimal hyperparameters of a DL model often needs high computational cost and human expertise. To mitigate the above issue, evolutionary computation (EC) as a powerful heuristic search approach has shown significant merits in the automated design of DL models, so-called evolutionary deep learning (EDL). This paper aims to analyze EDL from the perspective of automated machine learning (AutoML). Specifically, we firstly illuminate EDL from machine learning and EC and regard EDL as an optimization problem. According to the DL pipeline, we systematically introduce EDL methods ranging from feature engineering, model generation, to model deployment with a new taxonomy (i.e., what and how to evolve/optimize), and focus on the discussions of solution representation and search paradigm in handling the optimization problem by EC. Finally, key applications, open issues and potentially promising lines of future research are suggested. This survey has reviewed recent developments of EDL and offers insightful guidelines for the development of EDL.