Evolutionary optimization of cosmological parameters using metropolis
  acceptance criterion
  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 Lambda CDM 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.
