Based on negative correlation learning and evolutionary learning, this paper presents evolutionary ensembles with negative correlation learning (EENCL) to address the issues of automatic determination of the number of individual neural networks (NNs) in an ensemble and the exploitation of the interaction between individual NN design and combination. The idea of EENCL is to encourage di erent individual NNs in the ensemble to learn di erent parts or aspects of the training data so that the ensemble can better learn the entire training data. The cooperation and specialization among di erent individual NNs are considered during the individual NN design. This provides an opportunity for di erent NNs to interact with each other and to specialize. Experiments on two real-world problems demonstrate that EENCL can produce NN ensembles with good generalization ability.
Also, the negative correlation learning to generate negatively correlated individual neural network is proposed in REF .
Evolutionary ensembles with negative correlation learning
IEEE Trans. Evolutionary Computation
Computer Science