SSGA & EDA based feature selection and weighting for face recognition
In this paper, we compare genetic and evolutionary feature selection (GEFeS) and weighting (GEFeW) using a number of biometric datasets. GEFeS and GEFeW have been implemented as instances of Steady-State Genetic and Estimation of Distribution Algorithms. Our results show that GEFeS and GEFeW dramatically improve recognition accuracy as well as reduce the number of features needed for facial recognition. Our results also show that the Estimation of Distribution Algorithm implementation of GEFeW has the best overall performance.