title: Improving the adaptability of simulated evolutionary swarm robots in dynamically changing environments.
abstract: 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.
authors.affiliation: Department of Plant Systems Biology, VIB, Ghent, Belgium; Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium; Department of Microbial and Molecular Systems, KU Leuven, Leuven, Belgium.
authors.forename: Yao
authors.initials: Y
authors.lastname: Yao
authors.affiliation: Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium; Department of Microbial and Molecular Systems, KU Leuven, Leuven, Belgium; Department of Information Technology, iMinds, Ghent University, Ghent, Belgium.
authors.forename: Kathleen
authors.initials: K
authors.lastname: Marchal
authors.affiliation: Department of Plant Systems Biology, VIB, Ghent, Belgium; Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium; Department of Genetics, Genomics Research Institute, University of Pretoria, Pretoria, South Africa.
authors.forename: Yves
authors.initials: Y
authors.lastname: Van de Peer
mesh_terms: D000222:Adaptation, Physiological; D005075:Biological Evolution; D003198:Computer Simulation; D004777:Environment; D005246:Feedback; D053263:Gene Regulatory Networks; D016571:Neural Networks, Computer; D012371:Robotics