Searching the Landscape of Flux Vacua with Genetic Algorithms
This work addresses the challenge of finding flux vacua with interesting phenomenological properties in string theory, but it appears incremental as it applies an existing method to a known problem.
The authors tackled the problem of exploring the landscape of type IIB flux vacua by using genetic algorithms, showing they can efficiently scan for viable solutions in symmetric T^6 and conifold regions, and compared them to other methods like breeding mechanisms and random walks.
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.