Genetic Algorithm for SU(N) gauge theory on a 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.
 Publication:

arXiv eprints
 Pub Date:
 August 1998
 DOI:
 10.48550/arXiv.heplat/9808001
 arXiv:
 arXiv:heplat/9808001
 Bibcode:
 1998hep.lat...8001A
 Keywords:

 High Energy Physics  Lattice;
 Neural and Evolutionary Computing
 EPrint:
 rev.tex form, 9 pages, 12 figures