Community Detection Based on an Improved Genetic Algorithm

When the traditional genetic algorithm was used to solve the community detection problem, it was not easy to avoid the problems of low efficiency and slow convergent speed. To be aim at these problems, a improved genetic algorithm which is based on the immune mechanism was proposed in this paper. In this new algorithm, the immune mechanism was used to ensure the diversity of population. Meanwhile, a improved character encoding was adopted to further reduce the search space. The results shows that the shortcomings of slow convergent speed and low efficiency could be overcome by using the improved genetic algorithm to solve these problems, compared with the traditional genetic algorithm.

[1]  Dayou Liu,et al.  Genetic Algorithm with Local Search for Community Mining in Complex Networks , 2010, 2010 22nd IEEE International Conference on Tools with Artificial Intelligence.

[2]  He Dong,et al.  Genetic Algorithm with Local Search for Community Detection in Large-scale Complex Networks , 2011 .

[3]  Chunguang Zhou,et al.  Community Mining in Complex Networks---Clustering Combination Based Genetic Algorithm: Community Mining in Complex Networks---Clustering Combination Based Genetic Algorithm , 2010 .

[4]  D. Lusseau,et al.  The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations , 2003, Behavioral Ecology and Sociobiology.

[5]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  Tang Xu-qing.  New method for determining optimal number of clusters in K-means clustering algorithm , 2010 .

[7]  Fang Wu,et al.  Finding communities in linear time: a physics approach , 2003, ArXiv.

[8]  Maoguo Gong,et al.  Memetic algorithm for community detection in networks. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[9]  M E J Newman,et al.  Fast algorithm for detecting community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[10]  Clara Pizzuti,et al.  A Multi-objective Genetic Algorithm for Community Detection in Networks , 2009, 2009 21st IEEE International Conference on Tools with Artificial Intelligence.

[11]  He Dong.  Community Mining in Complex Networks—Clustering Combination Based Genetic Algorithm , 2010 .

[12]  Haifeng Du,et al.  A genetic algorithm with local search strategy for improved detection of community structure , 2010 .

[13]  Clara Pizzuti,et al.  Community detection in social networks with genetic algorithms , 2008, GECCO '08.

[14]  Konstantin Avrachenkov,et al.  Cooperative Game Theory Approaches for Network Partitioning , 2017, COCOON.

[15]  Haluk Bingol,et al.  Community Detection in Complex Networks Using Genetic Algorithms , 2006, 0711.0491.

[16]  Yi Wang,et al.  A Genetic Algorithm for Detecting Communities in Large-Scale Complex Networks , 2010, Adv. Complex Syst..

[17]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[18]  Yuan Hui.  Community structure division in complex networks based on gene expression programming algorithm , 2012 .