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We use Genetic Algorithms to extract information from several cosmologicalprobes, such as the type Ia supernovae (SnIa), the Baryon Acoustic Oscillations(BAO) and the growth rate of matter perturbations. This is done by implementinga model independent and bias-free reconstruction of the various scales anddistances that characterize the data, like the luminosity $d_L(z)$ and theangular diameter distance $d_A(z)$ in the SnIa and BAO data, respectively, orthe dependence with redshift of the matter density $\om_m(a)$ in the growthrate data, $f\sigma_8(z)$. These quantities can then be used to reconstruct theexpansion history of the Universe, and the resulting Dark Energy (DE) equationof 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 completelyindependent of our prior bias. Furthermore, we use this method to test theEtherington relation, ie the well-known relation between the luminosity and theangular diameter distance, $\eta \equiv \frac{d_L(z)}{(1+z)^2 d_A(z)}$, whichis equal to 1 in metric theories of gravity. We find that the present data seemto 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 analyticallyestimate the errors on the reconstructed quantities by calculating a PathIntegral over all possible functions that may contribute to the likelihood. Weshow that this can be done regardless of the data being correlated oruncorrelated with each other and we also explicitly demonstrate that ourapproach is in good agreement with other error estimation techniques like theFisher Matrix approach and the Bootstrap Monte Carlo.

A new perspective on Dark Energy modeling via Genetic Algorithms