  This paper demonstrates that simple yet important characteristics of
coevolution can occur in evolutionary algorithms when only a few conditions are
met. We find that interaction-based fitness measurements such as fitness
(linear) ranking allow for a form of coevolutionary dynamics that is observed
when 1) changes are made in what solutions are able to interact during the
ranking process and 2) evolution takes place in a multi-objective environment.
This research contributes to the study of simulated evolution in a at least two
ways. First, it establishes a broader relationship between coevolution and
multi-objective optimization than has been previously considered in the
literature. Second, it demonstrates that the preconditions for coevolutionary
behavior are weaker than previously thought. In particular, our model indicates
that direct cooperation or competition between species is not required for
coevolution to take place. Moreover, our experiments provide evidence that
environmental perturbations can drive coevolutionary processes; a conclusion
that mirrors arguments put forth in dual phase evolution theory. In the
discussion, we briefly consider how our results may shed light onto this and
other recent theories of evolution.
