Abstract
To effectively optimize neural networks, we have proposed a co-evolutionary neuro evolution (NE) scheme, which selects an appropriate set oftraining examples adaptively for the current population of neuralnetworks, based on co-evolution of neural networks and examples. Thescheme performs better than the conventional NE ones, but it oftenloses the optimal networks identified previously, during the course ofthe co-evolution. To remedy the drawback, we propose an extendedversion of the scheme and show the effectiveness of the proposed scheme through its application to a function approximation problem.
