TY - GEN
T1 - Community detection using parallel genetic algorithms
AU - Song, Yulong
AU - Li, Jianwu
AU - Zhang, Xiao
AU - Liu, Chunxue
PY - 2012
Y1 - 2012
N2 - The main problem on community detection using traditional genetic algorithms (GA) lies in the slow speed of convergence. This paper attempts to apply parallel genetic algorithms (PGA) to explore community structure in complex networks in order to improve the efficiency of traditional genetic algorithms. Several different designing ways of PGA are discussed and compared. Experimental results based on the GN benchmark networks, LFR benchmark networks, and eight real-world networks, confirm the PGA with coarse-grained-master-slave hybrid model spends less time yet achieves higher accuracy than traditional genetic algorithms.
AB - The main problem on community detection using traditional genetic algorithms (GA) lies in the slow speed of convergence. This paper attempts to apply parallel genetic algorithms (PGA) to explore community structure in complex networks in order to improve the efficiency of traditional genetic algorithms. Several different designing ways of PGA are discussed and compared. Experimental results based on the GN benchmark networks, LFR benchmark networks, and eight real-world networks, confirm the PGA with coarse-grained-master-slave hybrid model spends less time yet achieves higher accuracy than traditional genetic algorithms.
UR - http://www.scopus.com/inward/record.url?scp=84874606102&partnerID=8YFLogxK
U2 - 10.1109/ICACI.2012.6463189
DO - 10.1109/ICACI.2012.6463189
M3 - Conference contribution
AN - SCOPUS:84874606102
SN - 9781467317436
T3 - 2012 IEEE 5th International Conference on Advanced Computational Intelligence, ICACI 2012
SP - 374
EP - 378
BT - 2012 IEEE 5th International Conference on Advanced Computational Intelligence, ICACI 2012
T2 - 2012 IEEE 5th International Conference on Advanced Computational Intelligence, ICACI 2012
Y2 - 18 October 2012 through 20 October 2012
ER -