An evolutionary technique based on K-Means algorithm for optimal clustering in RN

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Clustering: A neural network approach (2010, positive): “The local minimum problem can be eliminated by using global optimization methods such as the genetic algorithm (GA) (Bandyopadhyay & Maulik, 2002; Krishna & Murty, 1999), the simulated annealing (SA) (Bandyopadhyay, Maulik, & Pakhira, 2001), and a hybrid SA and evolutionary algorithm (EA) system…”
A Survey of Evolutionary Algorithms for Clustering (2009, methodology): “[79], Bandyopadhyay and Maulik [10], Krishna and Murty [83], and Lu et al.”
An artificial bee colony approach for clustering (2010, methodology): “Its performance is compared with GA (Murthy & Chowdhury, 1996) and KGA (Bandyopadhyay & Maulik, 2002) algorithm.”
Differential evolution and particle swarm optimisation in partitional clustering (2006, background): “Many studies have shown that this approach is more robust in converging towards the optimal partition than classic partitional algorithms (e.g. Maulik and Bandyopadhyay 2000, Chiou and Lan, 2001, Bandyopadhyay and Maulik 2002a, Paterlini and Minerva 2003 ).”