  Submodular functions allow to model many real-world optimisation problems.
This paper introduces approaches for computing diverse sets of high quality
solutions for submodular optimisation problems. We first present diversifying
greedy sampling approaches and analyse them with respect to the diversity
measured by entropy and the approximation quality of the obtained solutions.
Afterwards, we introduce an evolutionary diversity optimisation approach to
further improve diversity of the set of solutions. We carry out experimental
investigations on popular submodular benchmark functions that show that the
combined approaches achieve high quality solutions of large diversity.
