  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.
