# Evolving neural networks with genetic algorithms to study the String Landscape

## TL;DR

This paper explores the use of genetically evolved neural networks to analyze the string landscape, focusing on classification, realization of computations, and prediction of complex mathematical outcomes in string phenomenology.

## Contribution

It introduces a method of dynamically evolving neural networks via genetic algorithms for versatile applications in string landscape analysis.

## Key findings

- Neural networks can classify models based on physical features.
- Evolved networks can implement complex computations more efficiently.
- Predictive models can estimate outcomes of computationally intensive string theory calculations.

## Abstract

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 such that the neural network performs best for the application we are interested in. We study three areas in which neural networks can be applied: to classify models according to a fixed set of (physically) appealing features, to find a concrete realization for a computation for which the precise algorithm is known in principle but very tedious to actually implement, and to predict or approximate the outcome of some involved mathematical computation which performs too inefficient to apply it, e.g. in model scans within the string landscape. We present simple examples that arise in string phenomenology for all three types of problems and discuss how they can be addressed by evolving neural networks from genetic algorithms.