Speciated neural networks evolved with fitness sharing technique

In order to develop effective evolutionary artificial neural networks (EANNs) we have to address the questions on how to evolve EANNs more efficiently and how to achieve the best performance from the ANNs evolved. Most of the previous works, however, do not utilize all the information obtained with several ANNs but choose the one best network in the last generation. Some recent works indicate that making use of population information by combining ANNs in the last generation can improve the performance, because they can complement each other to construct effective multiple neural networks. In this paper, we propose a new method of evolving multiple speciated neural networks by fitness sharing which helps to optimize multi-objective functions with genetic algorithms. Experiments with the breast cancer data from UCI benchmark datasets show that the proposed method can produce more speciated ANNs and improve the performance by combining the only representative individuals.