Theoretical physics

Algorithm

Demography

Pure mathematics

Statistics

Metallurgy

Materials science

Hypersurface

Computer science

Mathematical optimization

Brane cosmology

Physics

Organic chemistry

Mathematics

Genetic algorithm

Fitness landscape

Population

Sociology

Conifold

Chemistry

Flux (metallurgy)

Random walk

Machine learning

Journal of High Energy Physics

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 $T^{6}$ 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.

Searching the landscape of flux vacua with genetic algorithms