Third International Conference on Natural Computation (ICNC 2007) (2007)
Haikou, Hainan, China
Aug. 24, 2007 to Aug. 27, 2007
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICNC.2007.440
Kaikuo Xu , Sichuan University, China
Changjie Tang , Sichuan University, China
Yintian Liu , Sichuan University, China
Chuan Li , Sichuan University, China
Jiang Wu , Sichuan University, China
Jun Zhu , National Center for Birth Defects Monitoring, China
Li Dai , National Center for Birth Defects Monitoring, China
This study applies clustering in population selection to improve the efficiency of evolutionary algorithms. The main contributions include: (a) Proposes a novel selection framework that uses the number of clusters for a population as the measurement the population diversity. (b) Proposes clustering-ranking selection, an instance of this framework, and discusses its mathematical principle by PD-SP equation. (c) Gives experiments over CLPSO (Comprehensive Learning Particle Swarm Optimization). Experiment result shows that the proposed selection method outperforms canonical exponential ranking on all the sixteen-benchmark functions for both 10-D and 30-D problems except a function for 30-D problem.
J. Wu et al., "Improving Selection Methods for Evolutionary Algorithms by Clustering," Third International Conference on Natural Computation (ICNC 2007)(ICNC), Haikou, Hainan, China, 2007, pp. 742-746.