The traditional fitness function based methodology of artificial
evolution is argued to be inadequate for the construction of entities
with behaviors novel to their designers. Evolutionary emergence via
natural selection (without an explicit fitness function) is the way
forward. This paper further considers the question of what to evolve,
the focus being on principles of developmental modularity in neural
networks. To develop and test the ideas, an artificial world
containing autonomous organisms has been created and is described.
Results show the developmental system to be well suited to long-term
incremental evolution. Novel emergent strategies are identified both
from an observer's perspective and in terms of their neural
mechanisms.