CosmoGen: A genetic algorithm framework for the exploration of dark energy dynamics

The standard Lambda cold dark matter ($\Lambda$CDM) paradigm of the physical Universe suffers from well-known conceptual problems and is challenged by observational data. Alternative models exist in the literature, both phenomenological and physically motivated, but many of them suffer from similar or new problems. We propose a method to mechanically generate alternative models in a data-informed procedure tuned to mitigate specific problems. We implemented a computational framework, dubbed CosmoGen, based on evolutionary algorithms for symbolic regression. The evolutionary process is guided by the computation of structure formation and background cosmological quantities. As a proof-of-concept, we applied the procedure to the specific case of dark energy fluid models and asked the framework to generate models capable of alleviating the cosmological tensions $S_8$ and $H_0$. The system generated models with high fitness values, and through a Bayesian analysis of an illustrative model, we show that the model indeed alleviates the tensions, even though the Bayes factor indicates a weaker preference for $\Lambda$CDM.