Evolutionary optimization of an experimental apparatus

TL;DR

This paper presents an automated optimization method using a genetic algorithm for complex cold atom experiments, significantly reducing manual effort and efficiently handling high-dimensional, noisy parameter spaces.

Contribution

It introduces a flexible, robust genetic algorithm based on Differential Evolution for optimizing multiple correlated parameters in experimental setups.

Findings

Optimized 21 correlated parameters in cold atom experiments.

Proved robustness against local maxima and experimental noise.

Demonstrated broad applicability to various experimental optimization tasks.

Abstract

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.