实验装置的进化优化
Evolutionary optimization of an experimental apparatus
摘要：
近几十年来，冷原子实验变得越来越复杂。虽然计算机控制大多数参数，但优化主要是手动完成的。这对于具有未知相关性的高维参数空间是一项耗时的任务。在这里，我们使用基于差分进化的遗传算法来自动化这个过程。我们证明，该算法优化了21个相关参数，它对局部最大值和实验噪声是鲁棒的。该算法灵活，易于实现。因此，所提出的方案可以应用于广泛的实验优化任务。
英文摘要：
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.
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