Stochastic optimization of a cold atom experiment using a genetic algorithm
نویسندگان
چکیده
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. © 2008 American Institute of Physics. DOI: 10.1063/1.3058756
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