A new perspective on Dark Energy modeling via Genetic Algorithms
  We use Genetic Algorithms to extract information from several cosmological
probes, such as the type Ia supernovae (SnIa), the Baryon Acoustic Oscillations
(BAO) and the growth rate of matter perturbations. This is done by implementing
a model independent and bias-free reconstruction of the various scales and
distances that characterize the data, like the luminosity $d_L(z)$ and the
angular diameter distance $d_A(z)$ in the SnIa and BAO data, respectively, or
the dependence with redshift of the matter density $\om_m(a)$ in the growth
rate data, $f\sigma_8(z)$. These quantities can then be used to reconstruct the
expansion history of the Universe, and the resulting Dark Energy (DE) equation
of state $w(z)$ in the context of FRW models, or the mass radial function
$\om_M(r)$ in LTB models. In this way, the reconstruction is completely
independent of our prior bias. Furthermore, we use this method to test the
Etherington relation, ie the well-known relation between the luminosity and the
angular diameter distance, $\eta \equiv \frac{d_L(z)}{(1+z)^2 d_A(z)}$, which
is equal to 1 in metric theories of gravity. We find that the present data seem
to suggest a 3-$\sigma$ deviation from one at redshifts $z\sim 0.5$. Finally,
we present a novel way, within the Genetic Algorithm paradigm, to analytically
estimate the errors on the reconstructed quantities by calculating a Path
Integral over all possible functions that may contribute to the likelihood. We
show that this can be done regardless of the data being correlated or
uncorrelated with each other and we also explicitly demonstrate that our
approach is in good agreement with other error estimation techniques like the
Fisher Matrix approach and the Bootstrap Monte Carlo.
