# Multi-Objective Pruning for CNNs Using Genetic Algorithm

arXiv: https://arxiv.org/abs/1906.00399 · HTML: https://mlsanity.com/paper/1906.00399/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2019.

## Built on by (most cited first, 7 of 7)

- [Cross-Image Relational Knowledge Distillation for Semantic Segmentation](https://mlsanity.com/paper/2204.06986/) (2022)
- [Hierarchical Self-supervised Augmented Knowledge Distillation](https://mlsanity.com/paper/2107.13715/) (2021)
- [Online Knowledge Distillation via Mutual Contrastive Learning for Visual Recognition](https://mlsanity.com/paper/2207.11518/) (2022)
- [SGLP: A Similarity Guided Fast Layer Partition Pruning for Compressing Large Deep Models](https://mlsanity.com/paper/2410.14720/) (2024)
- [Knowledge Distillation Using Hierarchical Self-Supervision Augmented Distribution](https://mlsanity.com/paper/2109.03075/) (2021)
- [S-Rocket: Selective Random Convolution Kernels for Time Series Classification](https://mlsanity.com/paper/2203.03445/) (2022)
- [Localizing Semantic Patches for Accelerating Image Classification](https://mlsanity.com/paper/2206.03367/) (2022)

## Abstract (arXiv)

In this work, we propose a heuristic genetic algorithm (GA) for pruning convolutional neural networks (CNNs) according to the multi-objective trade-off among error, computation and sparsity. In our experiments, we apply our approach to prune pre-trained LeNet across the MNIST dataset, which reduces 95.42% parameter size and achieves 16× speedups of convolutional layer computation with tiny accuracy loss by laying emphasis on sparsity and computation, respectively. Our empirical study suggests that GA is an alternative pruning approach for obtaining a competitive compression performance. Additionally, compared with state-of-the-art approaches, GA is capable of automatically pruning CNNs based on the multi-objective importance by a pre-defined fitness function.