# Neural Network Pruning by Cooperative Coevolution

arXiv: https://arxiv.org/abs/2204.05639 · HTML: https://mlsanity.com/paper/2204.05639/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2022.

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

- [Structured Pruning for Deep Convolutional Neural Networks: A survey](https://mlsanity.com/paper/2303.00566/) (2023)
- [Effective Layer Pruning Through Similarity Metric Perspective](https://mlsanity.com/paper/2405.17081/) (2024)
- [Towards Optimal Compression: Joint Pruning and Quantization](https://mlsanity.com/paper/2302.07612/) (2023)
- [UPSCALE: Unconstrained Channel Pruning](https://mlsanity.com/paper/2307.08771/) (2023)
- [Learning Where to Edit Vision Transformers](https://mlsanity.com/paper/2411.01948/) (2024)
- [Evolutionary Computation and Explainable AI: A Roadmap to Understandable Intelligent Systems](https://mlsanity.com/paper/2406.07811/) (2024)
- [A Differentiable Framework for End-to-End Learning of Hybrid Structured Compression](https://mlsanity.com/paper/2309.13077/) (2023)
- [Reduced storage direct tensor ring decomposition for convolutional neural networks compression](https://mlsanity.com/paper/2405.10802/) (2024)
- [CCSRP: Robust Pruning of Spiking Neural Networks through Cooperative Coevolution](https://mlsanity.com/paper/2408.00794/) (2024)

## Abstract (arXiv)

Neural network pruning is a popular model compression method which can significantly reduce the computing cost with negligible loss of accuracy. Recently, filters are often pruned directly by designing proper criteria or using auxiliary modules to measure their importance, which, however, requires expertise and trial-and-error. Due to the advantage of automation, pruning by evolutionary algorithms (EAs) has attracted much attention, but the performance is limited for deep neural networks as the search space can be quite large. In this paper, we propose a new filter pruning algorithm CCEP by cooperative coevolution, which prunes the filters in each layer by EAs separately. That is, CCEP reduces the pruning space by a divide-and-conquer strategy. The experiments show that CCEP can achieve a competitive performance with the state-of-the-art pruning methods, e.g., prune ResNet56 for 63.42% FLOPs on CIFAR10 with -0.24% accuracy drop, and ResNet50 for 44.56% FLOPs on ImageNet with 0.07% accuracy drop.