# A Study of Genetic Algorithms for Hyperparameter Optimization of Neural Networks in Machine Translation

arXiv: https://arxiv.org/abs/2009.08928 · HTML: https://mlsanity.com/paper/2009.08928/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2020.

## Built on by (most cited first, 2 of 2)

- [GAAPO: Genetic Algorithmic Applied to Prompt Optimization](https://mlsanity.com/paper/2504.07157/) (2025)
- [Breeding Machine Translations: Evolutionary approach to survive and thrive in the world of automated evaluation](https://mlsanity.com/paper/2305.19330/) (2023)

## Abstract (arXiv)

With neural networks having demonstrated their versatility and benefits, the need for their optimal performance is as prevalent as ever. A defining characteristic, hyperparameters, can greatly affect its performance. Thus engineers go through a process, tuning, to identify and implement optimal hyperparameters. That being said, excess amounts of manual effort are required for tuning network architectures, training configurations, and preprocessing settings such as Byte Pair Encoding (BPE). In this study, we propose an automatic tuning method modeled after Darwin's Survival of the Fittest Theory via a Genetic Algorithm (GA). Research results show that the proposed method, a GA, outperforms a random selection of hyperparameters.