Improving the adaptability of simulated evolutionary swarm robots in dynamically changing environments

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dc.contributor.author Yao, Yao
dc.contributor.author Marchal, Kathleen
dc.contributor.author Van de Peer, Yves
dc.date.accessioned 2014-06-10T05:09:35Z
dc.date.available 2014-06-10T05:09:35Z
dc.date.issued 2014-03-05
dc.description.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. en_US
dc.description.librarian am2014 en_US
dc.description.sponsorship 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. en_US
dc.description.uri http://www.plosone.org en_US
dc.identifier.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 en_US
dc.identifier.issn 1932-6203
dc.identifier.other 10.1371/journal.pone.0090695
dc.identifier.uri http://hdl.handle.net/2263/40093
dc.language.iso en en_US
dc.publisher Public Library of Science en_US
dc.rights © 2014 Yao et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License en_US
dc.subject Evolutionary robotics en_US
dc.subject Changing environment en_US
dc.subject Swarm robots en_US
dc.subject Artificial genome en_US
dc.subject Gene regulatory network (GRN) en_US
dc.title Improving the adaptability of simulated evolutionary swarm robots in dynamically changing environments en_US
dc.type Article en_US


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