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In this paper a genetic algorithm based feature weighting methodology that is based on k-nn classifier is presented. The performance of the algorithm is evaluated in two folds. First of all, its differentiation capability among relevant and irrelevant features is evaluated. This is achieved by introducing dummy variables to a well known benchmark data set, namely the Iris Data. Secondly, its predictive performance is also evaluated. The results are encouraging in the sense that the proposed algorithm specifies lower weights to the dummy variables and yields high classification accuracy.