# Finding Strong Lottery Ticket Networks with Genetic Algorithms

arXiv: https://arxiv.org/abs/2411.04658 · HTML: https://mlsanity.com/paper/2411.04658/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2024.

## Built on by (most cited first, 3 of 3)

- [Guiding Evolutionary AutoEncoder Training with Activation-Based Pruning Operators](https://mlsanity.com/paper/2505.05138/) (2025)
- [Towards Scalable Lottery Ticket Networks using Genetic Algorithms](https://mlsanity.com/paper/2508.08877/) (2025)
- [Investigating the Lottery Ticket Hypothesis for Variational Quantum Circuits](https://mlsanity.com/paper/2509.11190/) (2025)

## Abstract (arXiv)

According to the Strong Lottery Ticket Hypothesis, every sufficiently large neural network with randomly initialized weights contains a sub-network which - still with its random weights - already performs as well for a given task as the trained super-network. We present the first approach based on a genetic algorithm to find such strong lottery ticket sub-networks without training or otherwise computing any gradient. We show that, for smaller instances of binary classification tasks, our evolutionary approach even produces smaller and better-performing lottery ticket networks than the state-of-the-art approach using gradient information.