Genetic Algorithms and the Search for Viable String Vacua
High Energy Physics - Theory
2015-06-19 v2 High Energy Physics - Phenomenology
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.
Keywords
Cite
@article{arxiv.1404.7359,
title = {Genetic Algorithms and the Search for Viable String Vacua},
author = {Steven Abel and John Rizos},
journal= {arXiv preprint arXiv:1404.7359},
year = {2015}
}
Comments
10 figures, 24 pages, JHEP version