  The presence of functional diversity within a group has been demonstrated to
lead to greater robustness, higher performance and increased problem-solving
ability in a broad range of studies that includes insect groups, human groups
and swarm robotics. Evolving group diversity however has proved challenging
within Evolutionary Robotics, requiring reproductive isolation and careful
attention to population size and selection mechanisms. To tackle this issue, we
introduce a novel, decentralised, variant of the MAP-Elites illumination
algorithm which is hybridised with a well-known distributed evolutionary
algorithm (mEDEA). The algorithm simultaneously evolves multiple diverse
behaviours for multiple robots, with respect to a simple token-gathering task.
Each robot in the swarm maintains a local archive defined by two pre-specified
functional traits which is shared with robots it come into contact with. We
investigate four different strategies for sharing, exploiting and combining
local archives and compare results to mEDEA. Experimental results show that in
contrast to previous claims, it is possible to evolve a functionally diverse
swarm without geographical isolation, and that the new method outperforms mEDEA
in terms of the diversity, coverage and precision of the evolved swarm.
