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. ; The authors acknowledge the support of the European Commission within the Work Program ‘‘Future and Emergent Technologies Proactive’’ under the grant agreement no. 216342 (Symbrion) and Ghent University (Multidisciplinary Research Partnership ‘Bioinformatics: from nucleotides to networks’. YVdP acknowledges support from the European Research Council under the European Union’s Seventh Framework Programme (FP/2007-2013)/ERC Advanced Grant Agreement n. 322739 - DOUBLE-UP. KM acknowledges support from KU Leuven: GOA/08/011, project NATAR; Agentschap voor Innovatie door Wetenschap en Technologie (IWT): SBONEMOA; Fonds Wetenschappelijk Onderzoek-Vlaanderen (FWO) IOK-B9725-G.0329.09. ; http://www.plosone.org ; am2014