# AutoLR: An Evolutionary Approach to Learning Rate Policies

arXiv: https://arxiv.org/abs/2007.04223 · HTML: https://mlsanity.com/paper/2007.04223/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2020.

## Built on by (most cited first, 4 of 4)

- [Rethinking Learning Rate Tuning in the Era of Large Language Models](https://mlsanity.com/paper/2309.08859/) (2023)
- [Selecting and Composing Learning Rate Policies for Deep Neural Networks](https://mlsanity.com/paper/2210.12936/) (2022)
- [AI-Powered Cow Detection in Complex Farm Environments](https://mlsanity.com/paper/2501.02080/) (2025)
- [Dynamic Learning Rate Scheduling based on Loss Changes Leads to Faster Convergence](https://mlsanity.com/paper/2512.14527/) (2025)

## Abstract (arXiv)

The choice of a proper learning rate is paramount for good Artificial Neural Network training and performance. In the past, one had to rely on experience and trial-and-error to find an adequate learning rate. Presently, a plethora of state of the art automatic methods exist that make the search for a good learning rate easier. While these techniques are effective and have yielded good results over the years, they are general solutions. This means the optimization of learning rate for specific network topologies remains largely unexplored. This work presents AutoLR, a framework that evolves Learning Rate Schedulers for a specific Neural Network Architecture using Structured Grammatical Evolution. The system was used to evolve learning rate policies that were compared with a commonly used baseline value for learning rate. Results show that training performed using certain evolved policies is more efficient than the established baseline and suggest that this approach is a viable means of improving a neural network's performance.