Cosmological parameter estimation with Genetic Algorithms

Understand

Genetic algorithms are a powerful tool in optimization for single and multi-modal functions.

• This paper provides an overview of their fundamentals with some analytical examples.
• In addition, we explore how they can be used as a parameter estimation tool in cosmological models to maximize the likelihood function, complementing the analysis with the traditional Markov Chain Monte Carlo methods.
• We analyze that genetic algorithms provide fast estimates by focusing on maximizing the likelihood function, although they cannot provide confidence regions with the same statistical meaning as Bayesian approaches.