What can Machine Learning tell us about the background expansion of the
  Universe?
  Machine learning (ML) algorithms have revolutionized the way we interpret
data in astronomy, particle physics, biology and even economics, since they can
remove biases due to a priori chosen models. Here we apply a particular ML
method, the genetic algorithms (GA), to cosmological data that describes the
background expansion of the Universe, namely the Pantheon Type Ia supernovae
and the Hubble expansion history $H(z)$ datasets. We obtain model independent
and nonparametric reconstructions of the luminosity distance $d_L(z)$ and
Hubble parameter $H(z)$ without assuming any dark energy model or a flat
Universe. We then estimate the deceleration parameter $q(z)$, a measure of the
acceleration of the Universe, and we make a $\sim4.5\sigma$ model independent
detection of the accelerated expansion, but we also place constraints on the
transition redshift of the acceleration phase
$(z_{\textrm{tr}}=0.662\pm0.027)$. We also find a deviation from $\Lambda$CDM
at high redshifts, albeit within the errors, hinting toward the recently
alleged tension between the SnIa/quasar data and the cosmological constant
$\Lambda$CDM model at high redshifts $(z\gtrsim1.5)$. Finally, we show the GA
can be used in complementary null tests of the $\Lambda$CDM via reconstructions
of the Hubble parameter and the luminosity distance.
