  Novelty search has shown to be a promising approach for the evolution of
controllers for swarm robotics. In existing studies, however, the experimenter
had to craft a domain dependent behaviour similarity measure to use novelty
search in swarm robotics applications. The reliance on hand-crafted similarity
measures places an additional burden to the experimenter and introduces a bias
in the evolutionary process. In this paper, we propose and compare two
task-independent, generic behaviour similarity measures: combined state count
and sampled average state. The proposed measures use the values of sensors and
effectors recorded for each individual robot of the swarm. The characterisation
of the group-level behaviour is then obtained by combining the sensor-effector
values from all the robots. We evaluate the proposed measures in an aggregation
task and in a resource sharing task. We show that the generic measures match
the performance of domain dependent measures in terms of solution quality. Our
results indicate that the proposed generic measures operate as effective
behaviour similarity measures, and that it is possible to leverage the benefits
of novelty search without having to craft domain specific similarity measures.
