Evolutionary optimization of cosmological parameters using metropolis acceptance criterion

Abstract

We introduce a novel evolutionary method that takes leverage from the MCMC method that can be used for constraining the parameters and theoretical models of Cosmology. Unlike the MCMC technique, which is essentially a non-parallel algorithm by design, the newly proposed algorithm is able to obtain the full potential of multi-core machines. With this algorithm, we could obtain the best-fit parameters of the ΛCDM cosmological model and identify the discrepancy in the Hubble parameter H 0 . In the present work we discuss the design principle of this novel approach and also the results from the analysis of Pantheon, OHD and Planck datasets are reported here. The estimation of parameters shows significant consistency with the previously reported values as well as a higher computational performance compared to the other similar exercises.

Cosmological Parameter Estimation with Genetic Algorithms

“…With the presented study, we show the prospect of using them as a complement within cosmological data analysis. This is in agreement and complementary with the existing research that also focuses on the statistical applications of evolutionary computation [39,120]. In our case, we have not proposed a novel method or algorithm; however, we have analyzed how to use GAs so that they can complement a traditional analysis of cosmological data and be an alternative to optimize the likelihood function.…”