  We present a novel local improvement scheme for the perfectly balanced graph
partitioning problem. This scheme encodes local searches that are not
restricted to a balance constraint into a model allowing us to find
combinations of these searches maintaining balance by applying a negative cycle
detection algorithm. We combine this technique with an algorithm to balance
unbalanced solutions and integrate it into a parallel multi-level evolutionary
algorithm, KaFFPaE, to tackle the problem. Overall, we obtain a system that is
fast on the one hand and on the other hand is able to improve or reproduce most
of the best known perfectly balanced partitioning results ever reported in the
literature.
