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.

29 pages, 12 figures

References in corpus (8)

• Evolving neural networks with genetic algorithms to study the String Landscape
• A new perspective on Dark Energy modeling via Genetic Algorithms
• Branes with Brains: Exploring String Vacua with Deep Reinforcement Learning
• Genetic Algorithms and the Search for Viable String Vacua
• GAz: A Genetic Algorithm for Photometric Redshift Estimation
• Revealing systematics in phenomenologically viable flux vacua with reinforcement learning
• Heterotic String Model Building with Monad Bundles and Reinforcement Learning
• Cohomology Chambers on Complex Surfaces and Elliptically Fibered Calabi-Yau Three-folds

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