Community detection in complex networks using an improved genetic algorithm

Against the defects of stronger randomness and local optimal solution when the community detection in complex networks was made using the genetic algorithm,a new method of community detection in complex networks was presented using the genetic algorithm structure. The single-iteration label propagation method was utilized to make population initializing,a strategy of unified label crossover against crossing difficulty of string representation was proposed,and the directional mutation strategy was adopted to solve the defects of random mutation in the genetic algorithm. The experimental results show that for the typical computer-generated network structure and realworld network structure,the method can detect community structure more accurately. Compared with the classical algorithms,it has a higher precision of community detection and a faster convergence speed.