This paper describes a novel and adaptive dictionary method for face recognition using genetic algorithms (GAs) in determining the optimal basis for encoding human faces. In analogy to pursuit methods, our novel method is called Evolutionary Pursuit (EP), and it allows for different types of (non-orthogonal) bases. EP processes face images in a lower dimensional whitened PCA subspace. Directed but random rotations of the basis vectors in this subspace are searched by GAs where evolution is driven by a fitness function defined in terms of performance accuracy and class separation (scatter index). Accuracy indicates the extent to which learning has been successful so far, while the scatter index gives an indication of the expected fitness on future trials. As a result, our approach improves the face recognition performance compared to PCA, and shows better generalization abilities than the Fisher Linear Discriminant (FLD) based methods.