  We study possible applications of artificial neural networks to examine the
string landscape. Since the field of application is rather versatile, we
propose to dynamically evolve these networks via genetic algorithms. This means
that we start from basic building blocks and combine them such that the neural
network performs best for the application we are interested in. We study three
areas in which neural networks can be applied: to classify models according to
a fixed set of (physically) appealing features, to find a concrete realization
for a computation for which the precise algorithm is known in principle but
very tedious to actually implement, and to predict or approximate the outcome
of some involved mathematical computation which performs too inefficient to
apply it, e.g. in model scans within the string landscape. We present simple
examples that arise in string phenomenology for all three types of problems and
discuss how they can be addressed by evolving neural networks from genetic
algorithms.
