Enough is Enough : Learning to Stop in Generative Systems

Gene regulatory networks (GRNs) have been used to drive artificial generative systems. These systems must begin and then stop generation, or growth, akin to their biological counterpart. In nature, this process is controlled automatically as an organism reaches its mature form; in evolved generative systems, this is more typically controlled by hardcoded limits, which can be difficult to determine. Removing parameters from the evolutionary process and allowing stopping to occur naturally within an evolved system would allow for more natural and regulated growth. This paper illustrates that, within the appropriate context, the introduction of memory components into GRNs allows a stopping criterion to emerge. A Long Short-Term Memory style network was implemented as a GRN for an Evo-Devo generative system and was tested on one simple (single point target) and two more complex (point clouds) problems with and without structure. The novel LSTMGRN performed well in simple tasks to optimise stopping conditions, but struggled to manage more complex environments. This early work in self-regulating growth will allow for further research in more complex systems to allow the removal of hyperparameters and allowing the evolutionary system to stop dynamically and prevent organisms overshooting the optimal.