Evolutionary synthesis of collective behavior

In the present position paper, I explore biologically-inspired computational processes that allow complex high-level collective behaviors to arise from low-level artificial agents (swarmers) - automatically. In contrast to similar projects, I seek elimination of technical constraints that narrow the free development of biology-analogous behavioral patterns. The result of such swarm evolutions is a fascinating variety of biological, yet completely transparent, analyzable behavior. Results include the spontaneous evolution of an exploration strategy that recently has been mathematically proven to be the optimal one under the conditions given. The work (which is part of my diploma thesis [11]) originally contributes to the field of synthetic biology and the goal was to make evolution milestones in biological swarm collaboration visible. However, I feel that high-level behavior generation techniques can be migrated to the field of collaborative security and suggest approaches to do so.