Evolving Heterotic Gauge Backgrounds: Genetic Algorithms versus Reinforcement Learning

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.

arXiv comment29 pages, 12 figures

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• Machine Learning on generalized Complete Intersection Calabi-Yau Manifolds
• Generative AI for Brane Configurations and Coamoeba
• Calabi-Yau Four/Five/Six-folds as ${\mathbb{P}}_{\text{w}}^n$ Hypersurfaces: Machine Learning, Approximation, and Generation
• Learning 3-Manifold Triangulations
• Probing the Structure of String Theory Vacua with Genetic Algorithms and Reinforcement Learning
• String Model Building, Reinforcement Learning and Genetic Algorithms