An Investigation on Multi-Objective Optimization of Feedforward Neural Network Topology

Determining the appropriate network topology for feedforward neural classifiers is an interesting problem that is usually addressed through evaluation against given performance metrics, such as accuracy, precision, recall, and F1-score. In this paper, we consider three more evaluation metrics inspired by the semi-supervised learning field, i.e. cluster performance, smoothness performance, and graph based metrics. Leveraging an island genetic algorithm, we investigate the applicability and efficacy of the derived multi-objective optimization strategy for defining the parameters of a feedforward neural network. The described approach is evaluated in the context of a visual classification task, but can be applied in a vast range of pattern analysis problems.