CLUSTERING USING AN IMPROVED HYBRID GENETIC ALGORITHM
Yongguo Liu, Xiaorong Pu, Yi-Dong Shen, Yi Zhang, Xiaofeng Liao · International Journal of Artificial Intelligence Tools · 2007
In this article, a new genetic clustering algorithm called the Improved Hybrid Genetic Clustering Algorithm (IHGCA) is proposed to deal with the clustering problem under the criterion of minimum sum of squares clustering. In IHGCA, the improvement operation including five local iteration methods is developed to tune the individual and accelerate the convergence speed of the clustering algorithm, and the partition-absorption mutation operation is designed to reassign objects among different clusters. By experimental simulations, its superiority over some known genetic clustering methods is demonstrated.