  Many important problems can be regarded as maximizing submodular functions
under some constraints. A simple multi-objective evolutionary algorithm called
GSEMO has been shown to achieve good approximation for submodular functions
efficiently. While there have been many studies on the subject, most of
existing run-time analyses for GSEMO assume a single cardinality constraint. In
this work, we extend the theoretical results to partition matroid constraints
which generalize cardinality constraints, and show that GSEMO can generally
guarantee good approximation performance within polynomial expected run time.
Furthermore, we conducted experimental comparison against a baseline GREEDY
algorithm in maximizing undirected graph cuts on random graphs, under various
partition matroid constraints. The results show GSEMO tends to outperform
GREEDY in quadratic run time.
