Genetic Algorithm for SU(N) gauge theory on a lattice

TL;DR

This paper introduces a genetic algorithm approach for simulating SU(N) lattice gauge theories, demonstrating faster thermalization and comparable results to traditional methods like Metropolis and Heatbath on SU(2) models.

Contribution

It presents a novel genetic algorithm method for SU(N) lattice gauge theory simulation, offering improved thermalization speed over standard techniques.

Findings

GAs produce results consistent with MP and HB methods.

Thermalization speed of GAs is faster than simple MP.

Applicable to SU(2) on a 2D lattice.

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.