Modularity adaptation in cooperative coevolution of feedforward neural networks

Abstract

In this paper, an adaptive modularity cooperative coevolutionary framework is presented for training feedforward neural networks. The modularity adaptation framework is composed of different neural network encoding schemes which transform from one level to another based on the network error. The proposed framework is compared with canonical cooperative coevolutionary methods. The results show that the proposal outperforms its counterparts in terms of training time, success rate and scalability.