  Detecting communities in complex networks can shed light on the essential
characteristics and functions of the modeled phenomena. This topic has
attracted researchers of various fields from both academia and industry. Among
the different methods implemented for community detection, Genetic Algorithms
(GA) have become popular recently. Considering the drawbacks of the currently
used locus-based and solution-vector-based encodings to represent the
individuals, in this paper, we propose (1) a new node similarity-based encoding
method to represent a network partition as an individual named MST-based. Then,
we propose (2) a new Adaptive Genetic Algorithm for Community Detection, along
with (3) a new initial population generation function, and (4) a new adaptive
mutation function called sine-based mutation function. Using the proposed
method, we combine similarity-based and modularity-optimization-based
approaches to find the communities of complex networks in an evolutionary
framework. Besides the fact that the proposed representation scheme can avoid
meaningless mutations or disconnected communities, we show that the new initial
population generation function, and the new adaptive mutation function, can
improve the convergence time of the algorithm. Experiments and statistical
tests verify the effectiveness of the proposed method compared with several
classic and state-of-the-art algorithms.
