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.
Highlights
Accelerator physics deals with intricate systems which depend on many interrelated specifications/variables and physical quantities
We report on the successful application of genetic algorithm (GA) 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
This study demonstrates that the GA is very efficient in finding the near-optimal working point for the collider
Summary
Accelerator physics deals with intricate systems which depend on many interrelated specifications/variables and physical quantities. We demonstrate that GAs designed for multiobjective optimization are powerful when applied to single-objective problems, even problems that are difficult or computationally prohibitive to solve using standard nonlinear optimization techniques. For single-objective problems, GAs converge more quickly than standard nonlinear optimization techniques (when applicable) and are more efficient than systematic parameter scans. V we summarize the work presented, discuss its importance, and outline the possible future applications of GAs to other problems in accelerator physics
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