Enhancing Data Selection Using Genetic Algorithm

Genetic algorithms are becoming increasingly valuable in solving large-scale, realistic, difficult problems, and selecting replica with multiple selection criteria - availability, security and time- is one of these problems. In this paper, a rank based elitist clustering Genetic Algorithm is proposed named RRWSGA, which alleviates the problem of being trapped in local clustering centroids using k-mean. Simulation results show that the proposed RRWSGA, outperforms k-mean by 9%. Much better performance of RRWSGA is observed.