Innovative Applications of Genetic Algorithms to Problems in Accelerator Physics
- 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)
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.
- Research Organization:
- Thomas Jefferson National Accelerator Facility (TJNAF), Newport News, VA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC)
- DOE Contract Number:
- AC05-06OR23177
- OSTI ID:
- 1059995
- Report Number(s):
- JLAB-ACO-12-1649; DOE/OR/23177-2342; TRN: US1300359
- Journal Information:
- Physical Review Special Topics. Accelerators and Beams, Vol. 16, Issue 01; ISSN 1098-4402
- Country of Publication:
- United States
- Language:
- English
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