  We report on the application of an evolutionary algorithm (EA) to enhance
performance of an ultra-cold quantum gas experiment. The production of a
$^{87}$Rubidium Bose-Einstein condensate (BEC) can be divided into fundamental
cooling steps, specifically magneto optical trapping of cold atoms, loading of
atoms to a far detuned crossed dipole trap and finally the process of
evaporative cooling. The EA is applied separately for each of these steps with
a particular definition for the feedback the so-called fitness. We discuss the
principles of an EA and implement an enhancement called differential evolution.
Analyzing the reasons for the EA to improve \eg, the atomic loading rates and
increase the BEC phase-space density, yields an optimal parameter set for the
BEC production and enables us to reduce the BEC production time significantly.
Furthermore, we focus on how additional information about the experiment and
optimization possibilities can be extracted and how the correlations revealed
allow for further improvement. Our results illustrate that EAs are powerful
optimization tools for complex experiments and exemplify that the application
yields useful information on the dependence of these experiments on the
optimized parameters.
