# Evolutionary optimization of an experimental apparatus

arXiv: https://arxiv.org/abs/1305.4094 · HTML: https://mlsanity.com/paper/1305.4094/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2013.

## Built on by (most cited first, 1 of 1)

- [End-to-End Optimal Detector Design with Mutual Information Surrogates](https://mlsanity.com/paper/2503.14342/) (2025)

## Abstract (arXiv)

In recent decades, cold atom experiments have become increasingly complex. While computers control most parameters, optimization is mostly done manually. This is a time-consuming task for a high-dimensional parameter space with unknown correlations. Here we automate this process using a genetic algorithm based on Differential Evolution. We demonstrate that this algorithm optimizes 21 correlated parameters and that it is robust against local maxima and experimental noise. The algorithm is flexible and easy to implement. Thus, the presented scheme can be applied to a wide range of experimental optimization tasks.