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Genetic algorithm demystified for cosmological parameter estimation
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AI
DOI：10.1016/j.ascom.2025.101041.png)
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Abstract
En 中文
Genetic algorithm (GA) belong 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.
Keywords:
LUMINOUS RED GALAXIES
ARTIFICIAL-INTELLIGENCE
CONVERGENCE
SKY
AGE
Journal
A
IF:
1.8
Papers:
90
Citations:
0