  We present a novel distributed evolutionary algorithm, KaFFPaE, to solve the
Graph Partitioning Problem, which makes use of KaFFPa (Karlsruhe Fast Flow
Partitioner). The use of our multilevel graph partitioner KaFFPa provides new
effective crossover and mutation operators. By combining these with a scalable
communication protocol we obtain a system that is able to improve the best
known partitioning results for many inputs in a very short amount of time. For
example, in Walshaw's well known benchmark tables we are able to improve or
recompute 76% of entries for the tables with 1%, 3% and 5% imbalance.
