  It is common knowledge that there is no single best strategy for graph
clustering, which justifies a plethora of existing approaches. In this paper,
we present a general memetic algorithm, VieClus, to tackle the graph clustering
problem. This algorithm can be adapted to optimize different objective
functions. A key component of our contribution are natural recombine operators
that employ ensemble clusterings as well as multi-level techniques. Lastly, we
combine these techniques with a scalable communication protocol, producing a
system that is able to compute high-quality solutions in a short amount of
time. We instantiate our scheme with local search for modularity and show that
our algorithm successfully improves or reproduces all entries of the 10th
DIMACS implementation~challenge under consideration using a small amount of
time.
