  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. Additionally, we
examine the impact of tailoring the SLCS2 rule generator for specific
environmental conditions. We find this leads to over-fitting, as might be
expected, and thus conclude that for greatest environment flexibility a general
rule generator should be utilised.
