The generalized Kernel Linear Discriminant Analysis (KLDA) is the dimensionality reduction technique with class discrimination to map the vectors from the feature dimensional space to the lower dimensional space. In this paper, we propose to tune the unknown parameters of the generalized KLDA using genetic algorithm to map the vectors from the feature dimensional space to the lower dimensional space. Nearest mean classifier is used for classification. Experiments are performed on medical data using the genetic algorithm based GLDA and reported in this paper. As an average 5% increase in the detection rate is achieved using the genetic algorithm based GLDA when compared with the other kernel function based LDA.
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