  Population annealing (PA) is a population-based algorithm that is designed
for equilibrium simulations of thermodynamic systems with a rough free energy
landscape. It is known to be more efficient in doing so than standard Markov
chain Monte Carlo alone. The algorithm has a number of parameters that can be
fine-tuned to improve performance. While there is some theoretical and
numerical work regarding most of these parameters, there appears to be a gap in
the literature concerning the role of resampling in PA. Here, we present a
numerical comparison of a number of resampling schemes for PA simulations of
the 2D Ising model.
