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

Yao Yao (UGent) , Kathleen Marchal (UGent) and Yves Van de Peer (UGent)
(2014) PLOS ONE. 9(3).
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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.
Keywords
ARTIFICIAL NEURAL-NETWORKS, ADAPTATION, LIFE, GENES, IBCN

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Citation

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MLA
Yao, Yao, et al. “Improving the Adaptability of Simulated Evolutionary Swarm Robots in Dynamically Changing Environments.” PLOS ONE, vol. 9, no. 3, 2014, doi:10.1371/journal.pone.0090695.
APA
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). https://doi.org/10.1371/journal.pone.0090695
Chicago author-date
Yao, Yao, Kathleen Marchal, and Yves Van de Peer. 2014. “Improving the Adaptability of Simulated Evolutionary Swarm Robots in Dynamically Changing Environments.” PLOS ONE 9 (3). https://doi.org/10.1371/journal.pone.0090695.
Chicago author-date (all authors)
Yao, Yao, Kathleen Marchal, and Yves Van de Peer. 2014. “Improving the Adaptability of Simulated Evolutionary Swarm Robots in Dynamically Changing Environments.” PLOS ONE 9 (3). doi:10.1371/journal.pone.0090695.
Vancouver
1.
Yao Y, Marchal K, Van de Peer Y. Improving the adaptability of simulated evolutionary swarm robots in dynamically changing environments. PLOS ONE. 2014;9(3).
IEEE
[1]
Y. Yao, K. Marchal, and Y. Van de Peer, “Improving the adaptability of simulated evolutionary swarm robots in dynamically changing environments,” PLOS ONE, vol. 9, no. 3, 2014.
@article{4359014,
  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.}},
  articleno    = {{e90695}},
  author       = {{Yao, Yao and Marchal, Kathleen and Van de Peer, Yves}},
  issn         = {{1932-6203}},
  journal      = {{PLOS ONE}},
  keywords     = {{ARTIFICIAL NEURAL-NETWORKS,ADAPTATION,LIFE,GENES,IBCN}},
  language     = {{eng}},
  number       = {{3}},
  pages        = {{9}},
  title        = {{Improving the adaptability of simulated evolutionary swarm robots in dynamically changing environments}},
  url          = {{http://doi.org/10.1371/journal.pone.0090695}},
  volume       = {{9}},
  year         = {{2014}},
}
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