There are a few strategies for selecting the values of the temperatures (or betas, where $\beta=1/T$) in a parallel tempering MCMC (geometric, adaptive, aimed at a 0.234 temperature swap acceptance rate). What I have not found is a strategy for selecting the number of temperatures (replicas) that one should use.
Naively one could assume that increasing the number of temperatures as much as possible is better. However in Atchadé et al. (2011) the authors show an example where using either too few or too many temperatures is almost equally inefficient (with the addition that using too many increases the computation time). So there is a number of temperatures that gives better results, at least better mixing.
Is there such a strategy?