  Over the last decade, significant progress has been made in understanding
complex biological systems, however there have been few attempts at
incorporating this knowledge into nature inspired optimization algorithms. In
this paper, we present a first attempt at incorporating some of the basic
structural properties of complex biological systems which are believed to be
necessary preconditions for system qualities such as robustness. In particular,
we focus on two important conditions missing in Evolutionary Algorithm
populations; a self-organized definition of locality and interaction epistasis.
We demonstrate that these two features, when combined, provide algorithm
behaviors not observed in the canonical Evolutionary Algorithm or in
Evolutionary Algorithms with structured populations such as the Cellular
Genetic Algorithm. The most noticeable change in algorithm behavior is an
unprecedented capacity for sustainable coexistence of genetically distinct
individuals within a single population. This capacity for sustained genetic
diversity is not imposed on the population but instead emerges as a natural
consequence of the dynamics of the system.
