Red-Black Tree based NeuroEvolution of Augmenting Topologies

In Evolutionary Artificial Neural Networks (EANN), evolutionary algorithms are used to give an additional alternative to adapt besides learning, specially for connection weights training and architecture design, among others. A type of EANNs known as Topology and Weight Evolving Artificial Neural Networks (TWEANN) are used to evolve topology and weights. In this work, we introduce a new encoding on an implementation of NeuroEvolution of Augmenting Topologies (NEAT), a type of TWEANN, by adopting the Red-Black Tree (RBT) as the main data structure to store the connection genes instead of using a list. This new version of NEAT efficacy was tested using as case of study some data sets from the UCI database. The accuracy of networks obtained through the new version of NEAT were compared with the accuracy obtained from feed-forward artificial neural networks trained using back-propagation. These comparisons yielded that the accuracy were similar, and in some cases the accuracy obtained by the new version were better. Also, as the number of patterns increases, the average number of generations increases exponentially. Finall