  While it is relatively easy to imitate and evolve natural swarm behavior in
simulations, less is known about the social characteristics of simulated,
evolved swarms, such as the optimal (evolutionary) group size, why individuals
in a swarm perform certain actions, and how behavior would change in swarms of
different sizes. To address these questions, we used a genetic algorithm to
evolve animats equipped with Markov Brains in a spatial navigation task that
facilitates swarm behavior. The animats' goal was to frequently cross between
two rooms without colliding with other animats. Animats were evolved in swarms
of various sizes. We then evaluated the task performance and social behavior of
the final generation from each evolution when placed with swarms of different
sizes in order to evaluate their generalizability across conditions. According
to our experiments, we find that swarm size during evolution matters: animats
evolved in a balanced swarm developed more flexible behavior, higher fitness
across conditions, and, in addition, higher brain complexity.
