Structural and Parametric Evolution of Continuous-Time Recurrent Neural Networks

Neuroevolution comprehends the class of methods responsible for evolving neural network topologies and weights by means of evolutionary algorithms. Despite their good performance in several control tasks, most of these methods use variations of simple sigmoidal neurons. Recent investigations have shown the potential applicability of more realistic neuron models, opening new perspectives for the next generation of neuroevolutionary methods. This work aims to extend a recent method known as NEAT to evolve continuous-time recurrent neural networks (CTRNNs). The proposed model is compared with previous methods on a control benchmark test. Preliminary results reveal some advantages when evolving general CTRNNs over traditional models.