Niche-based cooperative co-evolutionary ensemble neural network for classification.

Recently, artificial neural networks have been widely used for classification. It is important to optimize the weight parameters and topological structure of the neural network simultaneously. These two tasks are interdependent and should be solved at the same time to achieve a better result. However, existing works cannot balance the accuracy and diversity of neural networks very well. In this paper, a cooperative co-evolutionary algorithm is proposed to simultaneously evolve artificial neural network topology, neuron attributes, and connection weights. In the proposed algorithm, two effective strategies are proposed. First, the niche-based strategy is used in the evolutionary and cooperative process to refine the local search ability. In this way, a set of candidate networks with a higher level of output diversity is obtained. Second, a two-step comparison scheme is designed to acquire a compact ensemble network. Moreover, a fully connected weights matrix crossover scheme is used to avoid destroying the network structure. The proposed algorithm is tested on the benchmark classification problems in the UCI machine learning repository and compared with other state-of-the-art methods. The experimental results show that the proposed niche-based cooperative co-evolutionary ensemble neural network has a higher capability of generalization compared with other methods in six of nine kinds of classification problems. Furthermore, the proposed ensemble neural network has relatively low complexity. • A niche-based cooperative co-evolutionary algorithm is developed to evolve ANN. • A fully connected matrix crossover strategy is used to protect the network structure. • A set of networks with different structures can improve the performance of ensemble. [ABSTRACT FROM AUTHOR]