Modified Decomposition Framework and Algorithm for Many-objective Topology and Weight Evolution of Neural Networks
Adham Salih, Amiram Moshaiov · 2021
This paper presents a modified decomposition framework to support the Many-Objective Topology and Weight Evolution of Artificial Neural Networks (MaO-TWEANNs). Next, an algorithm, which is termed NEWS/D, is devised using the proposed framework. To validate its optimization capabilities, a numerical study is carried out. The performed numerical study includes demonstration problems ranging from three to seven objectives for which the ideal points are known. Finally, an additional numerical study is performed with respect to a possible real-life application. The latter study suggests that evolving class experts for multi-class classification problems could be enhanced using NEWS/D in a non-intuitive approach.