Searching the landscape of flux vacua with genetic algorithms
Alex Cole (Department of Physics, University of Wisconsin, 1150 University Avenue, Madison, WI, 53706, U.S.A.); Andreas Schachner (Department of Physics, University of Wisconsin, 1150 University Avenue, Madison, WI, 53706, U.S.A.); Gary Shiu (Department of Physics, University of Wisconsin, 1150 University Avenue, Madison, WI, 53706, U.S.A.)
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.
