  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.
