摘要
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.
| 源语言 | 英语 |
|---|---|
| 主期刊名 | 2012 IEEE 5th International Conference on Advanced Computational Intelligence, ICACI 2012 |
| 页 | 374-378 |
| 页数 | 5 |
| DOI | |
| 出版状态 | 已出版 - 2012 |
| 活动 | 2012 IEEE 5th International Conference on Advanced Computational Intelligence, ICACI 2012 - Nanjing, 中国 期限: 18 10月 2012 → 20 10月 2012 |
出版系列
| 姓名 | 2012 IEEE 5th International Conference on Advanced Computational Intelligence, ICACI 2012 |
|---|
会议
| 会议 | 2012 IEEE 5th International Conference on Advanced Computational Intelligence, ICACI 2012 |
|---|---|
| 国家/地区 | 中国 |
| 市 | Nanjing |
| 时期 | 18/10/12 → 20/10/12 |
指纹
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Song, Y., Li, J., Zhang, X., & Liu, C. (2012). Community detection using parallel genetic algorithms. 在 2012 IEEE 5th International Conference on Advanced Computational Intelligence, ICACI 2012 (页码 374-378). 文章 6463189 (2012 IEEE 5th International Conference on Advanced Computational Intelligence, ICACI 2012). https://doi.org/10.1109/ICACI.2012.6463189