Hybridized Improved Genetic Algorithm with Variable Length Chromosome for Image Clustering
نویسندگان
چکیده
Clustering is a process of putting similar data into groups. This paper presents data clustering using improved genetic algorithm (IGA) in which an efficient method of crossover and mutation are implemented. Further it is hybridized with the popular NelderMead (NM) Simplex search and K-means to exploit the potentiality of both in the hybridized algorithm. The performance of hybrid approach is evaluated with few data clustering problems. Further a Variable Length IGA is proposed which optimally finds the clusters of benchmark image datasets and the performance is compared with K-means and GCUK[12].The results revealed are very encouraging with IGA and its hybridization with other algorithms.
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