Evolving neural networks using a dual representation with a combined crossover operator

A new approach to the evolution of neural networks is presented. A linear chromosome combined with a grid-based representation of the network, and a new crossover operator, allow the evolution of the architecture and the weights simultaneously. In the approach there is no need for a separate weight optimization procedure and networks with more than one type of activation function can be evolved. A pruning strategy is also introduced, which leads to the generation of solutions with varying degrees of complexity. Results of the application of the method to several binary classification problems are reported