Many-objective training of a multi-layer perceptron

In this paper, a many-objective training scheme for a multi-layer feed-forward neural network is studied. In this scheme, each training data set, or the average over sub-sets of the training data, provides a single objective. A recently proposed group of evolutionary many-objective optimization algorithms based on the NSGA-II algorithm have been examined with respect to the handling of such problem cases. A modified NSGA-II algorithm, using the norm of an individual as a secondary ranking assignment method, appeared to give the best results, even for a large number of objectives (up to 50 in this study). However, there was no notable increase in performance against the standard backpropagation algorithm, and a remarkable drop in performance for higher-dimensional feature spaces (dimension 30 in this study).