  The interaction networks of biological systems are known to take on several
non-random structural properties, some of which are believed to positively
influence system robustness. Researchers are only starting to understand how
these structural properties emerge, however suggested roles for component
fitness and community development (modularity) have attracted interest from the
scientific community. In this study, we apply some of these concepts to an
evolutionary algorithm and spontaneously organize its population using
information that the population receives as it moves over a fitness landscape.
More precisely, we employ fitness and clustering based driving forces for
guiding network structural dynamics, which in turn are controlled by the
population dynamics of an evolutionary algorithm. To evaluate the effect this
has on evolution, experiments are conducted on six engineering design problems
and six artificial test functions and compared against cellular genetic
algorithms and 16 other evolutionary algorithm designs. Our results indicate
that a self-organizing topology evolutionary algorithm exhibits surprisingly
robust search behavior with promising performance observed over short and long
time scales. After a careful analysis of these results, we conclude that the
coevolution between a population and its topology represents a powerful new
paradigm for designing robust search heuristics.
