Neural Networks Optimized by Genetic Algorithms in Cosmology

Understand

The applications of artificial neural networks in the cosmological field have shone successfully during the past decade, this is due to their great ability of modeling large amounts of datasets and complex nonlinear functions.

• However, in some cases, their use still remains controversial because their ease of producing inaccurate results when the hyperparameters are not carefully selected.
• In this paper, to find the optimal combination of hyperparameters to artificial neural networks, we propose to take advantage of the genetic algorithms.
• As a proof of the concept, we analyze three different cosmological cases to test the performance of the architectures achieved with the genetic algorithms and compare them with the standard process, consisting of a grid with all possible configurations.