Swarm Behaviour Evolution via Rule Sharing and Novelty Search

28 Oct 2019Phillip SmithRobert HunjetAldeida AletiAsad Khan

We present in this paper an exertion of our previous work by increasing the robustness and coverage of the evolution search via hybridisation with a state-of-the-art novelty search and accelerate the individual agent behaviour searches via a novel behaviour-component sharing technique. Via these improvements, we present Swarm Learning Classifier System 2.0 (SLCS2), a behaviour evolving algorithm which is robust to complex environments, and seen to out-perform a human behaviour designer in challenging cases of the data-transfer task in a range of environmental conditions... (read more)

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