Extending the Nelder-Mead algorithm for feature selection from brain networks

Centrifugation is often applied in laboratories and industries to increase the effective gravity on a particle and hence make it sediment faster. Based on this principle, one may extend the existing optimization techniques, which are driven by only gravitational force (objective values of discovered best solutions), and do not consider application of centrifugal force for faster convergence. We extended the Nelder-Mead's simplex algorithm, by applying an exponentially decaying centrifugal force on each of the computed vertices of the simplex. The proposed centrifugation technique was also applied on other optimization algorithms including differential evolution and gravitational search algorithms. It was seen that application of centrifugal force indeed enhanced the objective values obtained by of all the tested evolutionary algorithms. The comparative performance of the extended Nelder-Mead Algorithm was found to be better among all the tested algorithms. The algorithms were compared on the basis of the best obtained objective value after a fixed number of objective function evaluations (here 20 times the problem dimension). Testing was performed in the real world problem of EEG feature selection (from brain networks), for the classification of memory encoding versus recall using SVM. The average classification accuracy was found to be high (89.97%).