Genetic Algorithms and the Search for Viable String Vacua

TL;DR

This paper demonstrates that Genetic Algorithms significantly improve the efficiency of searching for viable string vacua with desired phenomenological properties, outperforming random searches by orders of magnitude.

Contribution

It introduces Genetic Algorithms as an effective search method for string vacua and shows their superiority over random searches in finding models with specific properties.

Findings

Genetic Algorithms are over 10,000 times more efficient than random search.

They successfully find rare string vacua models with specific features.

Non-deterministic search methods are essential for exploring complex string landscape.

Abstract

Genetic Algorithms are introduced as a search method for finding string vacua with viable phenomenological properties. It is shown, by testing them against a class of Free Fermionic models, that they are orders of magnitude more efficient than a randomised search. As an example, three generation, exophobic, Pati-Salam models with a top Yukawa occur once in every 10^{10} models, and yet a Genetic Algorithm can find them after constructing only 10^5 examples. Such non-deterministic search methods may be the only means to search for Standard Model string vacua with detailed phenomenological requirements.