  Large classes of standard single-field slow-roll inflationary models
consistent with the required number of e-folds, the current bounds on the
spectral index of scalar perturbations, the tensor-to-scalar ratio, and the
scale of inflation can be efficiently constructed using genetic algorithms. The
setup is modular and can be easily adapted to include further phenomenological
constraints. A semi-comprehensive search for sextic polynomial potentials
results in roughly O(300,000) viable models for inflation. The analysis of this
dataset reveals a preference for models with a tensor-to-scalar ratio in the
range 0.0001 < r < 0.0004. We also consider potentials that involve cosine and
exponential terms. In the last part we explore more complex methods of search
relying on reinforcement learning and genetic programming. While reinforcement
learning proves more difficult to use in this context, the genetic programming
approach has the potential to uncover a multitude of viable inflationary models
with new functional forms.
