Behavioural diversity has been demonstrated as beneficial in biological
social systems, such as insect colonies and human societies,
as well as artificial systems such as large-scale software and
swarm-robotics systems. Evolutionary swarm robotics is a popular
experimental platform for demonstrating the emergence of
various social phenomena and collective behaviour, including behavioural
diversity and specialization. However, from an automated
design perspective, the evolutionary conditions necessary to synthesize
optimal collective behaviours (swarm-robotic controllers)
that function across increasingly complex environments (difficult
tasks), remains unclear. Thus, we introduce a comparative study
of behavioural-diversity maintenance methods (swarm-controller
extension of the MAP-Elites algorithm) versus those without behavioural
diversity mechanisms (Steady-State Genetic Algorithm),
as a means to evolve suitable degrees of behavioural diversity over
increasingly difficult collective behaviour (sheep-dog herding) tasks.
In support of previous work, experiment results demonstrate that
behavioural diversity can be generated without specific speciation
mechanisms or geographical isolation in the task environment, although
the direct evolution of a functionally (behaviorally) diverse
swarm does not yield high task performance
