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Validation of Region-based Crossover for Clustering Problems

Jeevan F. D’Souza, C. Kelly Adams, Andrew Reed


The k-means algorithm is a widely used partitional clustering algorithm because of its simplicity and computational efficiency. One problem with the k-means algorithm is that the quality of partitions produced is highly dependent on the initial selection of centers. The problem of center selection has been tackled in the past using genetic algorithms (GA). One of the most effective GA for k-means clustering is the region-based genetic algorithm (RBGA). This research aimed at assessing the RBGA across a variety of cluster representatives and distance metrics. The experimental results show the superior performance of the RBGA, as compared to other popular genetic algorithm approaches, indicating that region-based crossover may prove an effective strategy across a broad range of clustering problems.


k-means algorithm, clustering, genetic algorithm, crossover operation, center selection

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