Augmenting the NEAT algorithm to improve its temporal processing capabilities

This paper is concerned with the incorporation of new time processing capacities to the Neuroevolution of Augmenting Topologies (NEAT) algorithm. This algorithm is quite popular within the robotics community for the production of trained neural networks without having to determine a priori their size and topology. However, and even though the algorithm can address temporal processing issues through its capacity of establishing feedback synaptic connections, that is, through recurrences, there are still instances where more precise time processing may go beyond its limits. In order to address these cases, in this paper we describe a new implementation of the NEAT algorithm where trainable synaptic time delays are incorporated into its toolbox. This approach is shown to improve the behavior of neural networks obtained using NEAT in many instances. Here, we provide some of these results using a series of typical complex time processing tasks related to chaotic time series modeling and consider an example of the integration of this new approach within a robotic cognitive architecture.