Evolutionary optimization of an experimental apparatus
This work addresses the time-consuming optimization for experimental physicists in cold atom setups, though it is incremental as it applies an existing method to a new domain.
The researchers tackled the manual optimization problem in cold atom experiments by automating it with a genetic algorithm based on Differential Evolution, resulting in successful optimization of 21 correlated parameters with robustness against local maxima and noise.
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.