TitleHistorical Markings in Neuroevolution of Augmenting Topologies Revisited
Publication TypeConference Paper
Year of Publication2017
AuthorsPastorek L, O'Neill M
EditorMartín-Vide C, Neruda R, Vega-Rodríguez MA
Conference NameTheory and Practice of Natural Computing: 6th International Conference, TPNC 2017, Prague, Czech Republic, December 18-20, 2017, Proceedings
PublisherSpringer International Publishing
Conference LocationCham
ISBN Number978-3-319-71069-3

Historical markings in the NEAT algorithm provides a powerful feature for easy genetic alignment of any networks in the population, and allows speciation to protect networks with novelties. The original approach incorporated in NEAT always generates a new record for a connection with a unique ID when the connection is proposed in a generation. However, because of this mechanism, identical novelties developed in different generations are associated with different IDs and are not recognized as matching connections between networks. Despite popularity of the NEAT algorithm, there has been no existing study, which empirically investigates impact of this encoding on behavioral dynamics. The aim of this study is: firstly, to theoretically discuss generation context-dependent and generation context-free definitions for innovations (GC vs. GC-F); secondly, experimentally compare them on an XOR experiment under different speciation scenarios.