Evolutionary optimization of an experimental apparatus

Autor:	Geisel, I., Cordes, K., Mahnke, J., Jöllenbeck, S., Ostermann, J., Arlt, J., Ertmer, W., Klempt, C.
Rok vydání:	2013
Předmět:	Quantum Physics Condensed Matter - Quantum Gases Computer Science - Neural and Evolutionary Computing
Zdroj:	Appl. Phys. Lett. 102, 214105 (2013)
Druh dokumentu:	Working Paper
DOI:	10.1063/1.4808213
Popis:	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. Comment: minor revision
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1305.4094 Zobrazit plný text záznamu View this record from Arxiv