Genetic algorithms and the analysis of SnIa data
  The Genetic Algorithm is a heuristic that can be used to produce model
independent solutions to an optimization problem, thus making it ideal for use
in cosmology and more specifically in the analysis of type Ia supernovae data.
In this work we use the Genetic Algorithms (GA) in order to derive a null test
on the spatially flat cosmological constant model $\Lambda$CDM. This is done in
two steps: first, we apply the GA to the Constitution SNIa data in order to
acquire a model independent reconstruction of the expansion history of the
Universe $H(z)$ and second, we use the reconstructed $H(z)$ in conjunction with
the Om statistic, which is constant only for the $\Lambda$CDM model, to derive
our constraints. We find that while $\Lambda$CDM is consistent with the data at
the $2\sigma$ level, some deviations from $\Lambda$CDM model at low redshifts
can be accommodated.
