  The immensity of the string landscape and the difficulty of identifying
solutions that match the observed features of particle physics have raised
serious questions about the predictive power of string theory. Modern methods
of optimisation and search can, however, significantly improve the prospects of
constructing the standard model in string theory. In this paper we scrutinise a
corner of the heterotic string landscape consisting of compactifications on
Calabi-Yau three-folds with monad bundles and show that genetic algorithms can
be successfully used to generate anomaly-free supersymmetric SO(10) GUTs with
three families of fermions that have the right ingredients to accommodate the
standard model. We compare this method with reinforcement learning and find
that the two methods have similar efficacy but somewhat complementary
characteristics.
