Cosmological Parameter Estimation With Genetic Algorithms

The search for the optimal values, maxima or minima, of a complex function is an essential task in several fields where mathematical modeling is required. Genetic algorithms (GAs) are a powerful tool for optimization problems since, under certain conditions, they guarantee to always find the best solution; moreover, they have the advantage of preventing the use of derivatives, unlike other optimization methods, which gives them a great robustness in high dimensional or more complex problems.

Inspired by natural evolution, these algorithms efficiently explore the vast and unknown search spaces. Their ability to solve complex and dynamical projects makes them valuable in several fields, for example in medicine, epidemic dynamical systems, geotechnics, market forecasts and industry. A very successful application is the optimization of neural networks, which are very large computational models, and genetic algorithms can help finding a good combination of the hyperparameters.

In recent years, with the accelerated development of computational resources, genetic algorithms, as well as other machine learning algorithms, have been exploited in several scientific fields and, in particular, have resulted in significant advances in understanding particle physics, astronomical information and cosmological phenomena. The main goal of this paper is to introduce the fundamentals of genetic algorithms. We include illustrative examples of optimization problems and their applications in cosmology. Particularly, we delve into using genetic algorithms to constrain the parameter space of dark energy models based on observational data.

Ricardo Medel-Esquivel, Isidro Gómez-Vargas, Alejandro A. Morales Sánchez, Ricardo García-Salcedo, J. Alberto Vázquez

Comments: 16 pages, 6 figures
Subjects: Cosmology and Nongalactic Astrophysics (astro-ph.CO)
Cite as: arXiv:2311.05699 [astro-ph.CO] (or arXiv:2311.05699v1 [astro-ph.CO] for this version)
https://doi.org/10.48550/arXiv.2311.05699
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Submission history
From: J. Alberto Vazquez JAV
[v1] Thu, 9 Nov 2023 19:17:08 UTC (2,044 KB)
https://arxiv.org/abs/2311.05699

Astrobiology, Genomics, Genetics,