Data-Driven Reconstruction of f (R, T) Gravity Using Genetic Algorithms

We investigate f (R, T) gravity, where R is the Ricci scalar and T the trace of the energy–momentum tensor, focusing on the subclass defined by f (R, T) = R + 2f (T). Instead of assuming a parametric form, we adopt a non-parametric reconstruction based on genetic algorithms (GA), a machine learning technique that does not rely on predefined models. Using Hubble parameter measurements from cosmic chronometers, baryon acoustic oscillations, and the Dark Energy Spectroscopic Instrument (DESI) data, we reconstruct H(z) in a model-independent way. This reconstruction allows us to derive both numerical and analytical forms of f (T) through the modified Friedmann equations. The analytic expression derived via GA provides an excellent fit to the numerical reconstruction. Furthermore, we compare the evolution of the Hubble parameter predicted by our model with that of the standard ΛCDM scenario (Planck), finding a good agreement for z ≲ 2. These results highlight the robustness of GA-based reconstructions and suggest that the functional form of f (R, T) obtained here may serve as a promising tool for further applications in cosmology and astrophysics.