Improving the adaptability of simulated evolutionary swarm robots in dynamically changing environments
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Date
Authors
Yao, Yao
Marchal, Kathleen
Van de Peer, Yves
Journal Title
Journal ISSN
Volume Title
Publisher
Public Library of Science
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.
Description
Keywords
Evolutionary robotics, Changing environment, Swarm robots, Artificial genome, Gene regulatory network (GRN)
Sustainable Development Goals
Citation
Yao Y, Marchal K, Van de Peer Y (2014) Improving the Adaptability of Simulated Evolutionary Swarm Robots in Dynamically Changing Environments. PLoS ONE 9(3): e90695. DOI: Environments. PLoS ONE 9(3): e90695. doi:10.1371/journal.pone.0090695