abstract = "Evolutionary algorithms that use embryogenesis in the
creation of individuals have several desirable
qualities. Such algorithms are able to create complex,
modular designs which can scale well to large problems.
However, the inner workings of developmental algorithms
have not been investigated as thoroughly as their
direct-encoding counterparts. More precisely, it would
be beneficial to look at how the rules used during
embryogenesis evolve alongside the phenotypes they
produced. This paper reports on such an investigation
into the evolution of a rule set for the growth of an
artificial neural network, and identifies several
aspects that are desirable for the genomes of a
developmental evolutionary algorithm.",