Abstract
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.
Our analyses suggest that the GC-F approach produces 40–50% less innovation records than the GC approach. Moreover, the GC algorithm exploits more innovation records from the register. However, the assumption about a higher number of species for the GC approach is observed to be true only for the first 30 generations. The difference represents a maximum of 10% decline of GC-F rates when compared to GC. The analysis of migratory patterns shows, that GC-F leads to higher migration to older species. However, differences in migration to younger species were minimal. In conclusion, the exuberant number of innovation records in the register in NEAT does not lead to critical behavioral differences.
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This research is based upon works supported by Science Foundation Ireland under grant 13/RC/2094 which is co-funded under the European Regional Development Fund through the Southern & Eastern Regional Operational Programme to Lero - the Irish Software Research Centre (www.lero.ie).
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Pastorek, L., O’Neill, M. (2017). Historical Markings in Neuroevolution of Augmenting Topologies Revisited. In: Martín-Vide, C., Neruda, R., Vega-Rodríguez, M. (eds) Theory and Practice of Natural Computing. TPNC 2017. Lecture Notes in Computer Science(), vol 10687. Springer, Cham. https://doi.org/10.1007/978-3-319-71069-3_19
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