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The paper demonstrates performance enhancement using selective cloning on evolutionary neural network over the conventional genetic algorithm and neural back propagation algorithm for data classification. Introduction of selective cloning improves the convergence rate of the genetic algorithm without compromising on the classification errors. The selective cloning is tested on five data sets. The Iris data problem is used as a bench-mark to compare the selective cloning technique with thedoi:10.1080/18756891.2010.9727735 fatcat:hnj4rkqbnzfura5qrzotavx3lm