  Community detection in complex networks is a topic of considerable recent
interest within the scientific community. For dealing with the problem that
genetic algorithm are hardly applied to community detection, we propose a
genetic algorithm with ensemble learning (GAEL) for detecting community
structure in complex networks. GAEL replaces its traditional crossover operator
with a multi-individual crossover operator based on ensemble learning.
Therefore, GAEL can avoid the problems that are brought by traditional
crossover operator which is only able to mix string blocks of different
individuals, but not able to recombine clustering contexts of different
individuals into new better ones. In addition, the local search strategy, which
makes mutated node be placed into the community where most of its neighbors
are, is used in mutation operator. At last, a Markov random walk based method
is used to initialize population in this paper, and it can provide us a
population of accurate and diverse clustering solutions. Those diverse and
accurate individuals are suitable for ensemble learning based multi-individual
crossover operator. The proposed GAEL is tested on both computer-generated and
real-world networks, and compared with current representative algorithms for
community detection in complex networks. Experimental results demonstrate that
GAEL is highly effective at discovering community structure.
