Machine learning 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 specific machine learning method , the genetic algorithms ( GA ) , to cosmological data that describes the background expansion of the Universe , the Pantheon Type Ia supernovae and the Hubble expansion history H ( z ) datasets .
We obtain model independent and non-parametric 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 \sim 4.5 \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 \pm 0.027 ) .
We also confirm a recently reported mild tension between the SnIa/quasar data and the cosmological constant \Lambda CDM model at high redshifts ( z \gtrsim 1.5 ) and finally , we show that the GA can be used in complementary null tests of the \Lambda CDM via reconstructions of the Hubble parameter and the luminosity distance .