Genetic Algorithm for SU(N) gauge theory on a lattice
Yamaguchi Azusa
Abstract
An Algorithm is proposed for the simulation of pure SU(N) lattice gauge theories based on Genetic Algorithms(GAs). Main difference between GAs and Metropolis methods(MPs) is that GAs treat a population of points at once, while MPs treat only one point in the searching space. This provides GAs with information about the assortment as well as the fitness of the evolution function and producting a better solution. We apply GAs to SU(2) pure gauge theory on a 2 dimensional lattice and show the results are consistent with those given by MP and Heatbath methods(HBs). Thermalization speed of GAs is especially faster than the simple MPs.
Create a lesson
Related papers
Evidence of Taste-Symmetry restoration for Kähler-Dirac fermions on Euclidean Dynamical Triangulations
Mingwei Dai, Walter Freeman, Mason Grieb et al.
Hamiltonian framework for Chiral Gauge Theories on a Disk Boundary
Srimoyee Sen
Finite-size scaling analysis of three dimensional Z(2) and O(2) spin models with non-vanishing symmetry breaking parameter
Jishnu Goswami, Frithjof Karsch, Sabarnya Mitra
Bjorken sum rule down to the elastic region from the lattice
J. A. Crawford, A. Hannaford-Gunn, R. Horsley et al.
Performance Portable SU(N) Lattice Gauge Theory Simulation with Kokkos
Wei Sun
Sampling SU(N) gauge theory on a 2D lattice from independent plaquettes via holonomies and corner reweighting
Javad Komijani