Evolving Neural Network Ensembles by Minimization of Mutual Information

Learning and evolution are two fundamental forms of adaptation. There has been a great interest in combining learning and evolution with neural networks in recent years. This paper presents a hybrid learning system for learning and designing of neural network ensembles based on negative correlation learning and evolutionary learning. The idea of the hybrid learning system is to regard the population of neural networks as an ensemble, and the evolutionary process as the design of neural network ensembles. Two fitness sharing techniques have been used in the evolutionary process. One is based on the covering set. The other is to use the concept of mutual information. The effectiveness of such hybrid learning approach was tested on two real-world problems.