  We studied the long-term dynamics of evolutionary Swarm Chemistry by
extending the simulation length ten-fold compared to earlier work and by
developing and using a new automated object harvesting method. Both macroscopic
dynamics and microscopic object features were characterized and tracked using
several measures. Results showed that the evolutionary dynamics tended to
settle down into a stable state after the initial transient period, and that
the extent of environmental perturbations also affected the evolutionary trends
substantially. In the meantime, the automated harvesting method successfully
produced a huge collection of spontaneously evolved objects, revealing the
system's autonomous creativity at an unprecedented scale.

Adaptation and Self-Organizing Systems;Multiagent Systems;Neural and Evolutionary Computing