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Innovative applications of genetic algorithms to problems in accelerator physics

Alicia Hofler1, Balša Terzić1,2, Matthew Kramer3, Anton Zvezdin4, Vasiliy Morozov1, Yves Roblin1, Fanglei Lin1, and Colin Jarvis5

  • 1Jefferson Lab, Newport News, Virginia 23606, USA
  • 2Center for Accelerator Science, Old Dominion University, Norfolk, Virginia 23529, USA
  • 3University of California, Berkeley, California 94720, USA
  • 4Stony Brook University, Stony Brook, New York 11794, USA
  • 5Macalester College, Saint Paul, Minnesota 55105, USA

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Phys. Rev. ST Accel. Beams 16, 010101 – Published 9 January, 2013

DOI: https://doi.org/10.1103/PhysRevSTAB.16.010101

Abstract

The genetic algorithm (GA) is a powerful technique that implements the principles nature uses in biological evolution 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 Continuous Electron Beam Accelerator Facility nuclear physics machine, the proposed Medium-energy Electron-Ion Collider at Jefferson Lab, and a radio frequency 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, include a newly devised enhancement which leads to improved convergence to the optimum, and make recommendations for future GA developments and accelerator applications.

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Phys. Rev. ST Accel. Beams 16, 010101– Published 9 January, 2013
  • Received 19 September 2012
Article part of CHORUS
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