# A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends

arXiv: https://arxiv.org/abs/2209.06399 · HTML: https://mlsanity.com/paper/2209.06399/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 215 references that are in the map.
- First posted on arXiv 14 Sep 2022.

## Builds on (most cited first, 18 of 18)

- [Regularized Evolution for Image Classifier Architecture Search](https://mlsanity.com/paper/1802.01548/) (2018)
- [Generalizing from a Few Examples: A Survey on Few-Shot Learning](https://mlsanity.com/paper/1904.05046/) (2019)
- [A Survey on Evolutionary Neural Architecture Search](https://mlsanity.com/paper/2008.10937/) (2020)
- [LightTrack: Finding Lightweight Neural Networks for Object Tracking via One-Shot Architecture Search](https://mlsanity.com/paper/2104.14545/) (2021)
- [Cascaded deep monocular 3D human pose estimation with evolutionary training data](https://mlsanity.com/paper/2006.07778/) (2020)
- [NSGANetV2: Evolutionary Multi-Objective Surrogate-Assisted Neural Architecture Search](https://mlsanity.com/paper/2007.10396/) (2020)
- [Neural Architecture Transfer](https://mlsanity.com/paper/2005.05859/) (2020)
- [A Genetic Programming Approach to Designing Convolutional Neural Network Architectures](https://mlsanity.com/paper/1704.00764/) (2017)
- [Evolving Deep Convolutional Neural Networks for Image Classification](https://mlsanity.com/paper/1710.10741/) (2017)
- [Automatically designing CNN architectures using genetic algorithm for image classification](https://mlsanity.com/paper/1808.03818/) (2018)
- [Multi-objective Evolutionary Federated Learning](https://mlsanity.com/paper/1812.07478/) (2018)
- [Real-time Federated Evolutionary Neural Architecture Search](https://mlsanity.com/paper/2003.02793/) (2020)
- [Evolving Deep Neural Networks by Multi-objective Particle Swarm Optimization for Image Classification](https://mlsanity.com/paper/1904.09035/) (2019)
- [Genetic U-Net: Automatically Designed Deep Networks for Retinal Vessel Segmentation Using a Genetic Algorithm](https://mlsanity.com/paper/2010.15560/) (2020)
- [Learning and Sharing: A Multitask Genetic Programming Approach to Image Feature Learning](https://mlsanity.com/paper/2012.09444/) (2020)
- [Surrogate-assisted Particle Swarm Optimisation for Evolving Variable-length Transferable Blocks for Image Classification](https://mlsanity.com/paper/2007.01556/) (2020)
- [Brain Programming is Immune to Adversarial Attacks: Towards Accurate and Robust Image Classification using Symbolic Learning](https://mlsanity.com/paper/2103.01359/) (2021)
- [Automated Design of Salient Object Detection Algorithms with Brain Programming](https://mlsanity.com/paper/2204.03722/) (2022)

## Built on by (most cited first, 9 of 9)

- [Genetic Programming-Based Evolutionary Deep Learning for Data-Efficient Image Classification](https://mlsanity.com/paper/2209.13233/) (2022)
- [Neuromorphic Vision-based Motion Segmentation with Graph Transformer Neural Network](https://mlsanity.com/paper/2404.10940/) (2024)
- [Generalizing Supervised Deep Learning MRI Reconstruction to Multiple and Unseen Contrasts using Meta-Learning Hypernetworks](https://mlsanity.com/paper/2307.06771/) (2023)
- [A Survey of Blockchain, Artificial Intelligence, and Edge Computing for Web 3.0](https://mlsanity.com/paper/2311.13731/) (2023)
- [A Tent L'evy Flying Sparrow Search Algorithm for Feature Selection: A COVID-19 Case Study](https://mlsanity.com/paper/2209.10542/) (2022)
- [MonoSparse-CAM: Efficient Tree Model Processing via Monotonicity and Sparsity in CAMs](https://mlsanity.com/paper/2407.11071/) (2024)
- [Evolution-Inspired Sample Competition for Deep Neural Network Optimization](https://mlsanity.com/paper/2604.12568/) (2026)
- [StereoFactory: A Unified Merging Framework for Robust Stereo Matching](https://mlsanity.com/paper/2606.17475/) (2026)
- [ROI-Gated SAHI: Content-Adaptive Slicing-Based Inference for Efficient Object Detection](https://mlsanity.com/paper/2608.23923/) (2026)

## Abstract (arXiv)

Computer vision (CV) is a big and important field in artificial intelligence covering a wide range of applications. Image analysis is a major task in CV aiming to extract, analyse and understand the visual content of images. However, image-related tasks are very challenging due to many factors, e.g., high variations across images, high dimensionality, domain expertise requirement, and image distortions. Evolutionary computation (EC) approaches have been widely used for image analysis with significant achievement. However, there is no comprehensive survey of existing EC approaches to image analysis. To fill this gap, this paper provides a comprehensive survey covering all essential EC approaches to important image analysis tasks including edge detection, image segmentation, image feature analysis, image classification, object detection, and others. This survey aims to provide a better understanding of evolutionary computer vision (ECV) by discussing the contributions of different approaches and exploring how and why EC is used for CV and image analysis. The applications, challenges, issues, and trends associated to this research field are also discussed and summarised to provide further guidelines and opportunities for future research.