Towards a model-independent reconstruction approach for late-time Hubble
  data
  Gaussian processes offers a convenient way to perform nonparametric
reconstructions of observational data assuming only a kernel which describes
the covariance between neighbouring points in a data set. We approach the
ambiguity in the choice of kernel in Gaussian processes with two methods -- (a)
approximate Bayesian computation with sequential Monte Carlo sampling and (b)
genetic algorithm -- and use the overall resulting method to reconstruct the
cosmic chronometers and supernovae type Ia data sets. The results have shown
that the Mat\'{e}rn$\left( \nu = 5/2 \right)$ kernel emerges on top of the
two-hyperparameter family of kernels for both cosmological data sets. On the
other hand, we use the genetic algorithm in order to select a most
naturally-fit kernel among a competitive pool made up of a ten-hyperparameters
class of kernels. Imposing a Bayesian information criterion-inspired measure of
the fitness, the results have shown that a hybrid of the Radial Basis Function
and the Mat\'{e}rn$\left( \nu = 5/2 \right)$ kernel best represented both data
sets. The kernel selection problem is not totally closed and may benefit from
further analysis using other strategies to resolve an optimal kernel for a
particular data set.
