Cookies

We use cookies to ensure that we give you the best experience on our website. By continuing to browse this repository, you give consent for essential cookies to be used. You can read more about our Privacy and Cookie Policy.


Durham Research Online
You are in:

Evolving Heterotic Gauge Backgrounds: Genetic Algorithms versus Reinforcement Learning

Abel, Steven and Constantin, Andrei and Harvey, Thomas R. and Lukas, Andre (2022) 'Evolving Heterotic Gauge Backgrounds: Genetic Algorithms versus Reinforcement Learning.', Fortschritte der Physik = Progress of physics., 70 (5). p. 2200034.

Abstract

The immensity of the string landscape and the difficulty of identifying solutions that match the observed features of particle physics have raised serious questions about the predictive power of string theory. Modern methods of optimisation and search can, however, significantly improve the prospects of constructing the standard model in string theory. In this paper we scrutinise a corner of the heterotic string landscape consisting of compactifications on Calabi-Yau three-folds with monad bundles and show that genetic algorithms can be successfully used to generate anomaly-free supersymmetric 𝑆𝑂(10) GUTs with three families of fermions that have the right ingredients to accommodate the standard model. We compare this method with reinforcement learning and find that the two methods have similar efficacy but somewhat complementary characteristics.

Item Type:Article
Full text:(VoR) Version of Record
Available under License - Creative Commons Attribution 4.0.
Download PDF (Early view)
(1545Kb)
Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1002/prop.202200034
Publisher statement:This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Date accepted:No date available
Date deposited:03 May 2022
Date of first online publication:16 March 2022
Date first made open access:03 May 2022

Save or Share this output

Export:
Export
Look up in GoogleScholar