We propose a new feature selection method based on distributed genetic algorithms and bi-coded genes. This solution uses homogeneous and heterogeneous population strategies to minimize the complexity and to accelerate the algorithm convergence. The importance rate is computed for each feature measure to estimate the contribution of each feature in the finale selected vector. A new fitness function was proposed to take into consideration the recognition rate relatively to the size of the selected features subset. Two genetic codes are used to represent each member; a binary code to represent when the corresponding feature was selected or not; the second real code was used to estimate the importance rate of the selected feature or the selection probability for the non selected feature.