  The matter power spectrum $P(k)$ is one of the main quantities connecting
observational and theoretical cosmology. Although for a fixed redshift this can
be numerically computed very efficiently by Boltzmann solvers, an analytical
description is always desirable. However, accurate fitting functions for $P(k)$
are only available for the concordance model. Taking into account that
forthcoming surveys will further constrain the parameter space of cosmological
models, it is also of interest to have analytical formulations for $P(k)$ when
alternative models are considered. Here, we use the genetic algorithms, a
machine learning technique, to find a parametric function for $P(k)$
considering several possible effects imprinted by modifications of gravity. Our
expression for the $P(k)$ of modified gravity shows a mean accuracy of around
1-2% when compared with numerical data obtained via modified versions of the
Boltzmann solver CLASS, and thus it represents a competitive formulation given
the target accuracy of forthcoming surveys.
