Evolving Neural Controllers Using a Dual Network Representation
João Carlos Figueira, Riccardo Poli · 2007
In this paper 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 our approach there is no need for a separate weight optimization procedure and networks with more than one type of activation function can be evolved. This paper describes the representation, the crossover operator, and reports on results of the application of the method to evolve a neural controller for the polebalancing problem.