Evolving neural networks with genetic algorithms to study the string landscape

A bstract We study possible applications of artificial neural networks to examine the string landscape. Since the field of application is rather versatile, we propose to dynamically evolve these networks via genetic algorithms. This means that we start from basic building blocks and combine them suc...

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Bibliographic Details
Published in:	The journal of high energy physics Vol. 2017; no. 8; pp. 1 - 20
Main Author:	Ruehle, Fabian
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2017 Springer Nature B.V SpringerOpen
Subjects:	Artificial neural networks Classical and Quantum Gravitation Computation Elementary Particles Evolution Genetic algorithms High energy physics Neural networks Phenomenology Physics Physics and Astronomy Quantum Field Theories Quantum Field Theory Quantum Physics Regular Article - Theoretical Physics Relativity Theory String Theory Strings Superstring Vacua Superstrings and Heterotic Strings Superstring Vacua Superstrings and Heterotic Strings
ISSN:	1029-8479, 1029-8479
Online Access:	Get full text
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