We explore a wide variety of behaviours possible with Developmental Graph Cellular Automata. We use novelty search to find more extreme types of behaviour in terms of transient length and attractor cycle length. This also serves as a proof-of-concept that the system is evolvable. We then examine in more detail some individual examples of interesting behaviour, particularly focusing on cases where the graph divides into two or more separate components.

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© 2023 Massachusetts Institute of Technology Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license
2023
Massachusetts Institute of Technology
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Issue Section:
General Conference