In [1], Alford et al. compared the performances of two Genetic and Evolutionary Methods (GEMs) for multibiometric feature selection and weighting. In this paper, we present two hybrid feature weighting/selection GEMs. Our results show that the hybrid GEMs outperform the GEMs presented in [1], using significantly fewer features while achieving practically the same recognition accuracy.
