  We employ an evolutionary algorithm to automatically optimize different
stages of a cold atom experiment without human intervention. This approach
closes the loop between computer based experimental control systems and
automatic real time analysis and can be applied to a wide range of experimental
situations. The genetic algorithm quickly and reliably converges to the most
performing parameter set independent of the starting population. Especially in
many-dimensional or connected parameter spaces the automatic optimization
outperforms a manual search.
