  In this work, we study the $d$-Hitting Set and Feedback Vertex Set problems
through the paradigm of finding diverse collections of $r$ solutions of size at
most $k$ each, which has recently been introduced to the field of parameterized
complexity [Baste et al., 2019]. This paradigm is aimed at addressing the loss
of important side information which typically occurs during the abstraction
process which models real-world problems as computational problems. We use two
measures for the diversity of such a collection: the sum of all pairwise
Hamming distances, and the minimum pairwise Hamming distance. We show that both
problems are FPT in $k + r$ for both diversity measures. A key ingredient in
our algorithms is a (problem independent) network flow formulation that, given
a set of `base' solutions, computes a maximally diverse collection of
solutions. We believe that this could be of independent interest.
