期刊
APPLIED PHYSICS LETTERS
卷 102, 期 21, 页码 -出版社
AMER INST PHYSICS
DOI: 10.1063/1.4808213
关键词
-
资金
- Centre for Quantum Engineering and Space-Time Research QUEST
- Deutsche Forschungsgemeinschaft (Research Training Group 1729)
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. (C) 2013 AIP Publishing LLC.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据