We present a framework of algorithms and techniques involving hierarchical genetic algorithms. These algorithms attempt to generate models and solutions at
both the structural and atomic levels simultaneously using atomic and global chromosomes. Complex adaptive emergent systems (CAESs) are comprised of
heterogeneous, interacting, adaptive agents that exhibit the properties of self-organization, emergence, and connectivity [Ahmed2005]. This paper
presents early work and is meant to serve as a starting point in the synthesis of complex adaptive emergent systems using HGAs. Two implementations are
discussed1 that exemplify the compositional nature of hierarchical genetic algorithms and their applicability to both modeling and iteratively generating
complex adaptive emergent systems.