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Cosmic Inflation and Genetic Algorithms

Abel, Steve A.; Constantin, Andrei; Harvey, Thomas R.; Lukas, Andre

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Andrei Constantin

Thomas R. Harvey

Andre Lukas


Large classes of standard single-field slow-roll inflationary models consistentwith the required number of e-folds, the current bounds on the spectral indexof scalar perturbations, the tensor-to-scalar ratio, and the scale of inflation canbe efficiently constructed using genetic algorithms. The setup is modular andcan be easily adapted to include further phenomenological constraints. Asemi-comprehensive search for sextic polynomial potentials results in∼(300,000) viable models for inflation. The analysis of this dataset revealsa preference for models with a tensor-to-scalar ratio in the range0.0001≤r≤0.0004. We also consider potentials that involve cosine andexponential terms. In the last part we explore more complex methods ofsearch relying on reinforcement learning and genetic programming. Whilereinforcement learning proves more difficult to use in this context, the geneticprogramming approach has the potential to uncover a multitude of viableinflationary models with new functional forms.


Abel, S. A., Constantin, A., Harvey, T. R., & Lukas, A. (2023). Cosmic Inflation and Genetic Algorithms. Fortschritte der Physik, 71(1),

Journal Article Type Article
Online Publication Date Oct 29, 2022
Publication Date 2023
Deposit Date Jan 16, 2023
Publicly Available Date Jan 16, 2023
Journal Fortschritte der Physik
Print ISSN 0015-8208
Electronic ISSN 1521-3978
Publisher Wiley-VCH Verlag
Peer Reviewed Peer Reviewed
Volume 71
Issue 1


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Copyright Statement
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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