By Topic

Community Mining in Complex Network Based on Parallel Genetic Algorithm

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Xilu Zhu ; Beijing Key Lab. of Intell. Telecommun. Software & Multimedia, Beijing Univ. of Posts & Telecommun., Beijing, China ; Bai Wang

Community mining has been the focus of many recent efforts on complex networks, and the genetic algorithm with low time-complexity is widely used in this discipline. To enhance the performance of genetic algorithm for community detection, the modified crossover operators which are more suitable for community detection is proposed in this paper, and the heuristic mutation operator based on local modularity is designed to avoid the blindness of random flip. Additionally, to avoid premature, an independent evolution model is implemented on Chain Map Reduce framework. The experimental results show that the distributed evolutionary model contributes to reduce the selection pressure and maintains the population's diversity. Moreover, the modified genetic operators improve the global optimization performance and quicken the convergence speed.

Published in:

Genetic and Evolutionary Computing (ICGEC), 2010 Fourth International Conference on

Date of Conference:

13-15 Dec. 2010