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The Approximate Bayesian Computation (ABC) algorithm considers natural selection in biology as a guiding principle for statistical model selection and parameter estimation. We take this ABC approach to cosmology and use it to infer which model anchored on a choice of a Hubble constant prior would be preferred by the data. We find in all of our runs that the Planck Hubble constant ($H_0 = 67.4 \pm 0.5$ km s$^{-1}$Mpc$^{-1}$) always emerge naturally selected by the ABC over the SH$0$ES estimate ($H_0 = 73.30 \pm 1.04$ km s$^{-1}$Mpc$^{-1}$). The result holds regardless of how we mix our data sets, including supernovae, cosmic chronometers, baryon acoustic oscillations, and growth data. Compared with the traditional MCMC, we find that the ABC always results with narrower cosmological constraints, but remain consistent inside the corresponding MCMC posteriors.