A genetic algorithm with gene rearrangement for K-means clustering

Authors:
Dong-Xia Chang;Xian-Da Zhang;Chang-Wen Zheng
Affiliations:
Tsinghua National Laboratory for Information Science and Technology, State Key Laboratory on Intelligent Technology and Systems, Department of Automation, Tsinghua University, Beijing 100084, Chin ...;Tsinghua National Laboratory for Information Science and Technology, State Key Laboratory on Intelligent Technology and Systems, Department of Automation, Tsinghua University, Beijing 100084, Chin ...;National Key Lab of Integrated Information System Technology, Institute of Software, Chinese Academy of Sciences, Beijing 100080, China
Venue:
Pattern Recognition
Year:
2009

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Abstract

In this paper, a new clustering algorithm based on genetic algorithm (GA) with gene rearrangement (GAGR) is proposed, which in application may effectively remove the degeneracy for the purpose of a more efficient search. A new crossover operator that exploits a measure of similarity between chromosomes in a population is also presented. Adaptive probabilities of crossover and mutation are employed to prevent the convergence of the GAGR to a local optimum. Using the real-world data sets, we compare the performance of our GAGR clustering algorithm with K-means algorithm and other GA methods. An application of the GAGR clustering algorithm in unsupervised classification of multispectral remote sensing images is also provided. Experiment results demonstrate that the GAGR clustering algorithm has high performance, effectiveness and flexibility.