Stochastic optimization of a cold atom experiment using a genetic algorithm
Atomic Physics
2009-03-17 v2
Abstract
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.
Cite
@article{arxiv.0810.4474,
title = {Stochastic optimization of a cold atom experiment using a genetic algorithm},
author = {Wolfgang Rohringer and Robert Buecker and Stephanie Manz and Thomas Betz and Christian Koller and Martin Goebel and Aurelien Perrin and Joerg Schmiedmayer and Thorsten Schumm},
journal= {arXiv preprint arXiv:0810.4474},
year = {2009}
}
Comments
4 pages, 3 figures