One of the important challenges in the field of evolutionary robotics is the development of systems that can adapt to a
changing environment. However, the ability to adapt to unknown and fluctuating environments is not straightforward.
Here, we explore the adaptive potential of simulated swarm robots that contain a genomic encoding of a bio-inspired gene
regulatory network (GRN). An artificial genome is combined with a flexible agent-based system, representing the activated
part of the regulatory network that transduces environmental cues into phenotypic behaviour. Using an artificial life
simulation framework that mimics a dynamically changing environment, we show that separating the static from the
conditionally active part of the network contributes to a better adaptive behaviour. Furthermore, in contrast with most
hitherto developed ANN-based systems that need to re-optimize their complete controller network from scratch each time
they are subjected to novel conditions, our system uses its genome to store GRNs whose performance was optimized under
a particular environmental condition for a sufficiently long time. When subjected to a new environment, the previous
condition-specific GRN might become inactivated, but remains present. This ability to store ‘good behaviour’ and to
disconnect it from the novel rewiring that is essential under a new condition allows faster re-adaptation if any of the
previously observed environmental conditions is reencountered. As we show here, applying these evolutionary-based
principles leads to accelerated and improved adaptive evolution in a non-stable environment.