  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.
