Are you familiar with NSGA-2? That was the one I used in my PhD work.

A lot of research in GP/SR went toward methods for balancing density and selection from the pareto front.

One of the bigger problems is growth of meaningless equation material to counteract other pressures in the algorithm, which itself leads to premature convergence on suboptimal solutions. I had some thoughts at the time about ways to penalize this during the growth phase in my own algo, but never got around to them. Mainly needs bookkeeping for contribution to the final equation output.