| Title |
Genetic Algorithm Enhanced by Machine Learning in Dynamic Aperture Optimization |
| Authors |
- Y. Li, W.X. Cheng, R.S. Rainer, L. Yu
BNL, Upton, Long Island, New York, USA
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| Abstract |
With the aid of machine learning techniques, the genetic algorithm has been enhanced and applied to the multi-objective optimization problem presented by the dynamic aperture of the NSLS-II Ring. During the evolution employed by the genetic algorithm, the population is classified into different clusters. The clusters with top average fitness are given elite status. Intervention is implemented by repopulating some potentially competitive candidates based on the accumulated data. These candidates replace randomly selected candidates among the original data pool. The average fitness of the population is improved while diversity is not lost. The quality of the population increases and produces more competitive descendants accelerating the evolution process significantly. When identifying the distribution of optimal candidates, they appear to be located in isolated islands within the search space. Some of these optimal candidates have been experimentally confirmed at the NSLS-II storage ring. The machine learning techniques that exploit the genetic algorithm can also be used in other population-based optimization problems such as particle swarm algorithm.
|
| Funding |
This work was supported by Department of Energy Contract No. DE-SC0012704 |
| Paper |
download SAPAF01.PDF [1.597 MB / 7 pages] |
| Slides |
download SAPAF01_TALK.PDF [6.696 MB] |
| Export |
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| Conference |
ICAP2018 |
| Series |
International Computational Accelerator Physics Conference (13th) |
| Location |
Key West, FL, USA |
| Date |
20-24 October 2018 |
| Publisher |
JACoW Publishing, Geneva, Switzerland |
| Editorial Board |
Volker RW Schaa (GSI, Darmstadt, Germany); Kyoko Makino (MSU, East Lansing, MI, USA); Pavel Snopok (IIT, Chicago, IL, USA); Martin Berz (MSU, East Lansing, MI, USA) |
| Online ISBN |
978-3-95450-200-4 |
| Received |
15 October 2018 |
| Accepted |
24 October 2018 |
| Issue Date |
04 May 2019 |
| DOI |
doi:10.18429/JACoW-ICAP2018-SAPAF01 |
| Copyright |
 Published by JACoW Publishing under the terms of the Creative Commons Attribution 3.0 International license. Any further distribution of this work must maintain attribution to the author(s), the published article's title, publisher, and DOI. |
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