Innovative Applications of Genetic Algorithms to Problems in Accelerator Physics
Abstract
The genetic algorithm (GA) is a relatively new technique that implements the principles nature uses in biological evolution in order to optimize a multidimensional nonlinear problem. The GA works especially well for problems with a large number of local extrema, where traditional methods (such as conjugate gradient, steepest descent, and others) fail or, at best, underperform. The field of accelerator physics, among others, abounds with problems which lend themselves to optimization via GAs. In this paper, we report on the successful application of GAs in several problems related to the existing CEBAF facility, the proposed MEIC at Jefferson Lab, and a radio frequency (RF) gun based injector. These encouraging results are a step forward in optimizing accelerator design and provide an impetus for application of GAs to other problems in the field. To that end, we discuss the details of the GAs used, including a newly devised enhancement, which leads to improved convergence to the optimum and make recommendations for future GA developments and accelerator applications.
- Authors:
-
- Thomas Jefferson National Accelerator Facility, Newport News, VA (United States)
- Thomas Jefferson National Accelerator Facility, Newport News, VA (United States) and Old Dominion University, Norfolk, VA (United States)
- University of California, Berkeley, CA (United States)
- Stony Brook University, Stony Brook, NY (United States)
- Macalester College, Saint Paul, MN (United States)
- Publication Date:
- Research Org.:
- Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC)
- OSTI Identifier:
- 1059995
- Report Number(s):
- JLAB-ACO-12-1649; DOE/OR/23177-2342
Journal ID: ISSN 1098-4402; TRN: US1300359
- DOE Contract Number:
- AC05-06OR23177
- Resource Type:
- Journal Article
- Journal Name:
- Physical Review Special Topics. Accelerators and Beams
- Additional Journal Information:
- Journal Volume: 16; Journal Issue: 01; Journal ID: ISSN 1098-4402
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 43 PARTICLE ACCELERATORS
Citation Formats
Hofler, Alicia, Terzic, Balsa, Kramer, Matthew, Zvezdin, Anton, Morozov, Vasiliy, Roblin, Yves, Lin, Fanglei, and Jarvis, Colin. Innovative Applications of Genetic Algorithms to Problems in Accelerator Physics. United States: N. p., 2013.
Web. doi:10.1103/PhysRevSTAB.16.010101.
Hofler, Alicia, Terzic, Balsa, Kramer, Matthew, Zvezdin, Anton, Morozov, Vasiliy, Roblin, Yves, Lin, Fanglei, & Jarvis, Colin. Innovative Applications of Genetic Algorithms to Problems in Accelerator Physics. United States. https://doi.org/10.1103/PhysRevSTAB.16.010101
Hofler, Alicia, Terzic, Balsa, Kramer, Matthew, Zvezdin, Anton, Morozov, Vasiliy, Roblin, Yves, Lin, Fanglei, and Jarvis, Colin. 2013.
"Innovative Applications of Genetic Algorithms to Problems in Accelerator Physics". United States. https://doi.org/10.1103/PhysRevSTAB.16.010101.
@article{osti_1059995,
title = {Innovative Applications of Genetic Algorithms to Problems in Accelerator Physics},
author = {Hofler, Alicia and Terzic, Balsa and Kramer, Matthew and Zvezdin, Anton and Morozov, Vasiliy and Roblin, Yves and Lin, Fanglei and Jarvis, Colin},
abstractNote = {The genetic algorithm (GA) is a relatively new technique that implements the principles nature uses in biological evolution in order to optimize a multidimensional nonlinear problem. The GA works especially well for problems with a large number of local extrema, where traditional methods (such as conjugate gradient, steepest descent, and others) fail or, at best, underperform. The field of accelerator physics, among others, abounds with problems which lend themselves to optimization via GAs. In this paper, we report on the successful application of GAs in several problems related to the existing CEBAF facility, the proposed MEIC at Jefferson Lab, and a radio frequency (RF) gun based injector. These encouraging results are a step forward in optimizing accelerator design and provide an impetus for application of GAs to other problems in the field. To that end, we discuss the details of the GAs used, including a newly devised enhancement, which leads to improved convergence to the optimum and make recommendations for future GA developments and accelerator applications.},
doi = {10.1103/PhysRevSTAB.16.010101},
url = {https://www.osti.gov/biblio/1059995},
journal = {Physical Review Special Topics. Accelerators and Beams},
issn = {1098-4402},
number = 01,
volume = 16,
place = {United States},
year = {Tue Jan 01 00:00:00 EST 2013},
month = {Tue Jan 01 00:00:00 EST 2013}
}