Performance Enhancement of Data Classification using Selectively Cloned Genetic Algorithm for Neural Network

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 the conventional GA and the back-propagation algorithm. For comparative analysis, same neural network architecture is used for both the back propagation and the genetic algorithms. The selective cloning approach is based on the schema theorem. By using selective cloning, it has been shown that GA is 27.78% more efficient than the conventional GA and 83.33% more efficient than the back propagation approach. The results of selective cloning on other data sets are also discussed.