  In order to further improve the performance of current genetic algorithms
aiming at discovering communities, a local search based genetic algorithm GALS
is here proposed. The core of GALS is a local search based mutation technique.
In order to overcome the drawbacks of traditional mutation methods, the paper
develops the concept of marginal gene and then the local monotonicity of
modularity function Q is deduced from each nodes local view. Based on these two
elements, a new mutation method combined with a local search strategy is
presented. GALS has been evaluated on both synthetic benchmarks and several
real networks, and compared with some presently competing algorithms.
Experimental results show that GALS is highly effective and efficient for
discovering community structure.
