Cosmic Inflation and Genetic Algorithms

Large classes of standard single-field slow-roll inflationary models consistent with the required number e-folds, current bounds on spectral index scalar perturbations, tensor-to-scalar ratio, and scale inflation can be efficiently constructed using genetic algorithms. The setup is modular easily adapted to include further phenomenological constraints. A semi-comprehensive search for sextic polynomial potentials results in ∼ O ( 300 , 000 ) $\sim \mathcal {O}(300,000)$ viable inflation. analysis this dataset reveals a preference ratio range 0.0001 ≤ r 0.0004 $0.0001\le r\le 0.0004$ . We also consider that involve cosine exponential terms. In last part we explore more complex methods relying reinforcement learning programming. While proves difficult use context, programming approach has potential uncover multitude new functional forms.