Genetic Algorithms and Supernovae Type Ia Analysis
  We introduce genetic algorithms as a means to analyze supernovae type Ia data
and extract model-independent constraints on the evolution of the Dark Energy
equation of state. Specifically, we will give a brief introduction to the
genetic algorithms along with some simple examples to illustrate their
advantages and finally we will apply them to the supernovae type Ia data. We
find that genetic algorithms can lead to results in line with already
established parametric and non-parametric reconstruction methods and could be
used as a complementary way of treating SnIa data. As a non-parametric method,
genetic algorithms provide a model-independent way to analyze data and can
minimize bias due to premature choice of a dark energy model.
