Abstract
We introduce genetic algorithms as a means to analyze supernovaetype Ia data and extract model-independent constraints on theevolution of the Dark Energy equation of state w(z) ≡ PDE/ρDE. Specifically, we will give a briefintroduction to the genetic algorithms along with some simpleexamples to illustrate their advantages and finally we will applythem to the supernovae type Ia data. We find that geneticalgorithms can lead to results in line with already establishedparametric and non-parametric reconstruction methods and could beused as a complementary way of treating SNIa data. As anon-parametric method, genetic algorithms provide amodel-independent way to analyze data and can minimize bias due topremature choice of a dark energy model.
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