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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.10450 |
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| _version_ | 1866909957307236352 |
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| author | Bernardo, Reginald Christian Chen, Yun |
| author_facet | Bernardo, Reginald Christian Chen, Yun |
| contents | 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 $Λ$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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_10450 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Genetic algorithm demystified for cosmological parameter estimation Bernardo, Reginald Christian Chen, Yun Cosmology and Nongalactic Astrophysics Computational Physics Physics Education 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 $Λ$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. |
| title | Genetic algorithm demystified for cosmological parameter estimation |
| topic | Cosmology and Nongalactic Astrophysics Computational Physics Physics Education |
| url | https://arxiv.org/abs/2505.10450 |