  Intersecting branes provide a useful mechanism to construct particle physics
models from string theory with a wide variety of desirable characteristics. The
landscape of such models can be enormous, and navigating towards regions which
are most phenomenologically interesting is potentially challenging. Machine
learning techniques can be used to efficiently construct large numbers of
consistent and phenomenologically desirable models. In this work we phrase the
problem of finding consistent intersecting D-brane models in terms of genetic
algorithms, which mimic natural selection to evolve a population collectively
towards optimal solutions. For a four-dimensional ${\cal N}=1$ supersymmetric
type IIA orientifold with intersecting D6-branes, we demonstrate that
$\mathcal{O}(10^6)$ unique, fully consistent models can be easily constructed,
and, by a judicious choice of search environment and hyper-parameters,
$\mathcal{O}(30\%)$ of the found models contain the desired Standard Model
gauge group factor. Having a sizable sample allows us to draw some preliminary
landscape statistics of intersecting brane models both with and without the
restriction of having the Standard Model gauge factor.
