Innovative applications of genetic algorithms to problems in accelerator physics

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.

Machine based optimization using genetic algorithms in a storage ring

“…A more detailed discussion of these two parameters can be found in the literature such as Ref. [16]. In short, these tuning parameters control the probability density function of the likeness between parents and children.…”

“…We use two of the most popular genetic operators: real-coded simulated binary crossover [16,25] and polynomial mutation [16,26]. In each genetic operation, one of the two operators is chosen randomly but conforms to a predefined ratio.…”

“…Some of these applications of GAs in the particle accelerator community are well summarized in Ref. [16]. One core process of the GA is to evaluate objective functions from a given set of decision variables, i.e.…”

Model-independent particle accelerator tuning

“…In fact, MOGAs and GAs have been used to successfully optimize many aspects of particle accelerators, such as magnet and radio frequency (rf) cavity design [1], photoinjector design [2], damping ring design [3], storage ring dynamics [4], global optimization of a lattice [5], neutrino factory design [6], simultaneous optimization of beam emittance and dynamic aperture [7], and free electron laser linac drivers [8]. A thorough review of GA for accelerator physics applications is given in [9].…”

Adaptive method for electron bunch profile prediction

“…For dealing with many parameter systems, many optimization schemes [4], including in particular, genetic algorithms (GA), have been used very successfully, including the design of magnet and radio frequency (rf) cavities [5], photoinjectors [6], damping rings [7], storage ring dynamics [8], global optimization of a lattice [9], neutrino factory design [10], simultaneous optimization of beam emittance and dynamic aperture [11], free electron laser linac drivers [12] and various other accelerator physics applications [13]. The major benefit of GA-type searches is that they result in global optimization, at the cost of a lengthy search over a large range of the parameter space, and the result is only optimal relative to a known model.…”