We use a particular machine learning approach, called the genetic algorithms
    (GA), in order to place constraints on deviations from general relativity (GR)
    via a possible evolution of Newton’s constant $\mu\equiv
    G_\mathrm{eff}/G_\mathrm{N}$ and of the dark energy anisotropic stress $\eta$,
    both defined to be equal to one in GR. Specifically, we use a plethora of
    background and linear-order perturbations data, such as type Ia supernovae,
    baryon acoustic oscillations, cosmic chronometers, redshift space distortions
    and $E_g$ data. We find that although the GA is affected by the lower quality
    of the currently available data, especially from the $E_g$ data, the
    reconstruction of Newton’s constant is consistent with a constant value within
    the errors. On the other hand, the anisotropic stress deviates strongly from
    unity due to the sparsity and the systematics of the $E_g$ data. Finally, we
    also create synthetic data based on a next-generation survey and forecast the
    limits of any possible detection of deviations from GR. In particular, we use
    two fiducial models: one based on the cosmological constant $\Lambda$CDM model
    and another on a model with an evolving Newton’s constant, dubbed $\mu$CDM. We
    find that the GA reconstructions of $\mu(z)$ and $\eta(z)$ can be constrained
    to within a few percent of the fiducial models and in the case of the $\mu$CDM
    mocks, they can also provide a strong detection of several $\sigma$s, thus
    demonstrating the utility of the GA reconstruction approach.

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