Thanks for this file, very useful. I have one small problem with it though. I'm using the Lebesgue measure to track the progress of an 2-objective optimisation problem (both objectives are to be minimised). The idea being that if the hypervolume is increasing then the pareto front must be improving. But sometimes the Pareto front can become shorter, because new Pareto points dominate ones that were previously at the upper bound of each objective, and when this happens the hypervolume decreases. So in this case the pareto front is improving (because the new points dominate the old points) but the hypervolume is going down.
I can get around this problem by including the two solutions, from the full set of solutions, that have, respectively, the maximum value for each objective. These solutions are often not on the pareto front (since there are other designs that have a lower value for one or both objectives), so that breaks the rule in your Lebesgue measure that all solutions of F should be non-dominated. Should I worry about this?