Engineering solutions to problems has been the core of the technological development since the dawn of industrialization. Nowadays, we encounter an incredible number of problems that call for solutions of considerable complexity which can hardly be solved by such an direct hands-on approach. This is true for the realms of nature that we are about to explore (e.g. genetics) as well as for their application-driven, creative counterparts (e.g. biotechnology). Nature provides us with an infinitely broad (and deep) range of examples that solve complex problems, themselves showing incredible degrees of interaction and emergence. Swarms of social insects are one popular example in which a well-coordinated interplay of single entities allows for fascinating accomplishments. From a scientific point of view the simulation of swarms is especially interesting because it reflects time and space, it enables us to retrace the emergent patterns that we see in nature, and it allows for studying new approaches to solving our earthly problems. In the course of researching emergent appearances growth is a key element to complex solutions (e.g. neural growth, cell growth/differentiation, development of cities). Therefore, I am currently dedicated to abstract growth models with a focus on approaches that incorporate a swarm aspect in the sense of a three-dimensional multi-agent system. A big part of this research is the hunt for a universal generative genetic representation, for emergent patterns based on simple local interactions and the possibility of evolutionary developments.