Genetic algorithm demystified for cosmological parameter estimation

TL;DR

This paper explores the use of genetic algorithms as an alternative or complementary method to MCMC for estimating cosmological parameters, assessing their effectiveness with real cosmological data.

Contribution

It provides a pedagogical analysis of GA in cosmology, detailing hyperparameter effects and comparing results with traditional MCMC methods.

Findings

GA can effectively estimate cosmological parameters

Hyperparameters significantly influence GA performance

GA results are comparable to MCMC in accuracy

Abstract

Genetic algorithm (GA) belongs to a class of nature-inspired evolutionary algorithms that leverage concepts from natural selection to perform optimization tasks. In cosmology, the standard method for estimating parameters is the Markov chain Monte Carlo (MCMC) approach, renowned for its reliability in determining cosmological parameters. This paper presents a pedagogical examination of GA as a potential corroborative tool to MCMC for cosmological parameter estimation. Utilizing data sets from cosmic chronometers and supernovae with a curved $\Lambda$CDM model, we explore the impact of GA's key hyperparameters -- such as the fitness function, crossover rate, and mutation rate -- on the population of cosmological parameters determined by the evolutionary process. We compare the results obtained with GA to those by MCMC, analyzing their effectiveness and viability for cosmological application.