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Adaptive self-organizing organisms using a bio-inspired gene regulatory network controller: for the aggregation of evolutionary robots under a changing environment

Yao Yao UGent, Kathleen Marchal UGent and Yves Van de Peer UGent (2016) Handbook of research on design, control and modeling of swarm robotics. In Advances in Computational Intelligence and Robotics p.68-82
abstract
This work has explored 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 behavior. Using an Alife simulation framework that mimics a changing environment, we have shown that separating the static from the conditionally active part of the network contributes to a better adaptive behavior. This chapter describes the biologically inspired concept of GRNs to develop a distributed robot self-organizing approach. In particular, it shows that by using this approach, multiple swarm robots can aggregate to form a robotic organism that can adapt its configuration as a response to a dynamically changing environment. In addition, through the comparison of several different simulation experiments, the results illustrate the impact of evolutionary operators such as mutations and duplications on improving the adaptability of organisms.
Please use this url to cite or link to this publication:
author
organization
year
type
bookChapter
publication status
published
subject
book title
Handbook of research on design, control and modeling of swarm robotics
editor
Ying Tan
series title
Advances in Computational Intelligence and Robotics
pages
68 - 82
publisher
IGI Global
place of publication
Hershey, PA, USA
ISSN
2327-0411
ISBN
9781466695733
9781466695726
DOI
10.4018/978-1-4666-9572-6.ch003
project
Bioinformatics: from nucleotids to networks (N2N)
language
English
UGent publication?
yes
classification
B2
copyright statement
I have transferred the copyright for this publication to the publisher
id
7037094
handle
http://hdl.handle.net/1854/LU-7037094
date created
2016-01-14 09:36:01
date last changed
2017-04-27 08:42:09
@incollection{7037094,
  abstract     = {This work has explored 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 behavior. Using an Alife simulation framework that mimics a changing environment, we have shown that separating the static from the conditionally active part of the network contributes to a better adaptive behavior. This chapter describes the biologically inspired concept of GRNs to develop a distributed robot self-organizing approach. In particular, it shows that by using this approach, multiple swarm robots can aggregate to form a robotic organism that can adapt its configuration as a response to a dynamically changing environment. In addition, through the comparison of several different simulation experiments, the results illustrate the impact of evolutionary operators such as mutations and duplications on improving the adaptability of organisms.},
  author       = {Yao, Yao and Marchal, Kathleen and Van de Peer, Yves},
  booktitle    = {Handbook of research on design, control and modeling of swarm robotics},
  editor       = {Tan, Ying},
  isbn         = {9781466695733},
  issn         = {2327-0411},
  language     = {eng},
  pages        = {68--82},
  publisher    = {IGI Global},
  series       = {Advances in Computational Intelligence and Robotics},
  title        = {Adaptive self-organizing organisms using a bio-inspired gene regulatory network controller: for the aggregation of evolutionary robots under a changing environment},
  url          = {http://dx.doi.org/10.4018/978-1-4666-9572-6.ch003},
  year         = {2016},
}

Chicago
Yao, Yao, Kathleen Marchal, and Yves Van de Peer. 2016. “Adaptive Self-organizing Organisms Using a Bio-inspired Gene Regulatory Network Controller: For the Aggregation of Evolutionary Robots Under a Changing Environment.” In Handbook of Research on Design, Control and Modeling of Swarm Robotics, ed. Ying Tan, 68–82. Hershey, PA, USA: IGI Global.
APA
Yao, Yao, Marchal, K., & Van de Peer, Y. (2016). Adaptive self-organizing organisms using a bio-inspired gene regulatory network controller: for the aggregation of evolutionary robots under a changing environment. In Ying Tan (Ed.), Handbook of research on design, control and modeling of swarm robotics (pp. 68–82). Hershey, PA, USA: IGI Global.
Vancouver
1.
Yao Y, Marchal K, Van de Peer Y. Adaptive self-organizing organisms using a bio-inspired gene regulatory network controller: for the aggregation of evolutionary robots under a changing environment. In: Tan Y, editor. Handbook of research on design, control and modeling of swarm robotics. Hershey, PA, USA: IGI Global; 2016. p. 68–82.
MLA
Yao, Yao, Kathleen Marchal, and Yves Van de Peer. “Adaptive Self-organizing Organisms Using a Bio-inspired Gene Regulatory Network Controller: For the Aggregation of Evolutionary Robots Under a Changing Environment.” Handbook of Research on Design, Control and Modeling of Swarm Robotics. Ed. Ying Tan. Hershey, PA, USA: IGI Global, 2016. 68–82. Print.