  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.
