Communication networks are prevalent in both natural and artificial systems, enabling information and resource exchanges amongst system parts. In most systems, the network topology influences system performance, which, in turn, reshapes the network topology; hence creating one or several feedback cycles. Understanding how such system growth and restructuring processes function becomes critical in today's increasingly connected world. This paper describes a generic model of feedback-based growth and adaptation for systems with tree-like control topologies. Inspired by plant vascular morphogensis, the model is transferred here for studying the development and adaptation behaviour of business organisations, operating in fluctuating economic environments. Experimental results show the impact that various degrees of internal competition have on overall business growth, productivity and reactivity to changing business landscapes. The preliminary findings presented seem to fit existing economic studies on related topics. The proposed model offers a solid basis for studying morphogenetic processes and associated performance indicators, applied to tree-shaped business organisations, and transferable to further domains.

Morphogenesis in biological systems is controlled by the parameters encoded in the genomes and rules of interaction between different components of the system and environment. Several methods are proposed for developing morphology of artificial structures. Some of them are inspired by embryogenesis in biological organisms. Others use more abstract generative encodings such as variances of L-systems. Our approach to morphogenesis is based on the distribution of a common resource between competing components of a growing system. The novel distributed controller called Vascular Morphogenesis Controller (VMC) is inspired by the growth process of plants and more specifically the competition between different branches for developing vessels and thus for further growth. The initial algorithm is introduced for modular robots. Here we use it to solve a maze.

In this paper we report our ongoing work with evolving biohybrid societies. We develop robots that will be integrated in an animal society and will be accepted as a conspecific. Moreover, we want our robots to affect the behaviour of animals. We are using evolutionary algorithms to optimise robot controllers, where fitness is evaluated via measuring the effect a robot controller has on the animals. Several issues have to be considered: if the animals do not have a homogeneous behaviour several evaluations are needed to rule out outliers, and yet evaluating animal behaviour is a time consuming task. Besides the time it takes to record their behaviour, we have to take into account animal resting time, stimulus habituation, and feeding periods. Another factor that increases the task difficulty is robot heterogeneity, which is similar to the so called reality gap problem that occurs in evolving robot controllers in simulation. In our case, if we want a robust robot controller, we have to evaluate it in different robots. Overall, we found that doing online on-board evolutionary computation with robotic devices and animals is extremely challenging and we provide clues to avoid its major pitfalls.