  The linear matter power spectrum $P(k,z)$ connects theory with large scale
structure observations in cosmology. Its scale dependence is entirely encoded
in the matter transfer function $T(k)$, which can be computed numerically by
Boltzmann solvers, and can also be computed semi-analytically by using fitting
functions such as the well-known Bardeen-Bond-Kaiser-Szalay (BBKS) and
Eisenstein-Hu (EH) formulae. However, both the BBKS and EH formulae have some
significant drawbacks. On the one hand, although BBKS is a simple expression,
it is only accurate up to $10\%$, which is well above the $1\%$ precision goal
of forthcoming surveys. On the other hand, while EH is as accurate as required
by upcoming experiments, it is a rather long and complicated expression. Here,
we use the Genetic Algorithms (GAs), a particular machine learning technique,
to derive simple and accurate fitting formulae for the transfer function
$T(k)$. When the effects of massive neutrinos are also considered, our
expression slightly improves over the EH formula, while being notably shorter
in comparison.
