  In this talk I explained briefly the advantages of using genetic algorithms
on any measured data but specially astronomical ones. This kind of algorithms
are not only a better computational paradigm, but they also allow for a more
profound data treatment enhancing theoretical developments. As an example, I
will use the SNIa cosmological data to fit the extended metric theories of
gravity of Carranza et al. (2013, 2014) showing that the best parameters
combination deviate from theoretical predicted ones by a minimal amount. This
means that these kind of gravitational extensions are statistically robust and
show that no dark matter and/or energy is required to explain the observations.
