Searching the landscape of flux vacua with genetic algorithms
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Publication:2292468
DOI10.1007/JHEP11(2019)045zbMATH Open1429.83085arXiv1907.10072OpenAlexW3099185239WikidataQ126802242 ScholiaQ126802242MaRDI QIDQ2292468
Author name not available (Why is that?)
Publication date: 3 February 2020
Published in: (Search for Journal in Brave)
Abstract: In this paper, we employ genetic algorithms to explore the landscape of type IIB flux vacua. We show that genetic algorithms can efficiently scan the landscape for viable solutions satisfying various criteria. More specifically, we consider a symmetric as well as the conifold region of a Calabi-Yau hypersurface. We argue that in both cases genetic algorithms are powerful tools for finding flux vacua with interesting phenomenological properties. We also compare genetic algorithms to algorithms based on different breeding mechanisms as well as random walk approaches.
Full work available at URL: https://arxiv.org/abs/1907.10072
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