  Genetic regulation is a key component in development, but a clear
understanding of the structure and dynamics of genetic networks is not yet at
hand. In this work we investigate these properties within an artificial genome
model originally introduced by Reil. We analyze statistical properties of
randomly generated genomes both on the sequence- and network level, and show
that this model correctly predicts the frequency of genes in genomes as found
in experimental data. Using an evolutionary algorithm based on stabilizing
selection for a phenotype, we show that robustness against single base
mutations, as well as against random changes in initial network states that
mimic stochastic fluctuations in environmental conditions, can emerge in
parallel. Evolved genomes exhibit characteristic patterns on both sequence and
network level.
