Work

GAME : Genetic Algorithms with marginalised ensembles for model-independent reconstruction of cosmological quantities

Year: 2026

Type: article

Abstract: Genetic Algorithms (GA) are a versatile tool for stochastic optimisation and non-parametric symbolic regression, already widely used in cosmology. They are capable of reconstructing analytical functions directly from data points without introducing new physical models. A limitation of this approach is that while the reconstructed function is very efficient at reproducing the behaviour of the data points, non-observable quantities involving derivatives are particularly sensitive to stochasticity, hyperparameters, and the GA's chosen best-fit function, risking the algorithm getting stuck in a local minimum. In this work we propose an update to the GA methodology for the reconstruction of analytical functions that involves computing a weighted average of an ensemble of GA configurations ( GAME ). We define the weights via a quantity that accounts for both the goodness-of-fit of the points and the smoothness of the resulting function. We also present a practical method to analytically estimate and correct the errors on the averaged function by combining a path-integral approach with an ensemble variance. We demonstrate the improvement offered by GAME methodology on a generic test function. We then apply the new methodology to a non-parametric reconstruction of the Hubble rate H ( z ) using Cosmic Chronometers data and, assuming a flat Friedmann-Lemaître-Robertson-Walker background and General Relativity, we infer the corresponding dark energy equation of state w ( z ). As an additional test, we apply the updated methodology to reconstruct the distance modulus μ ( z ) from Type Ia Supernovae data and its corresponding derived H ( z ) and w ( z ). Finally, we show that current data produce results compatible with ΛCDM, and that future cosmological surveys will allow GA reinforced with GAME methodology to become an even more competitive tool for discriminating between different models.

Source: Journal of Cosmology and Astroparticle Physics

DOI: 10.1088/1475-7516/2026/08/063

Authors: Matteo Peronaci, Matteo Martinelli, Savvas Nesseris