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International Conference on Intelligent Computing

ICIC 2017: Intelligent Computing Theories and Application pp 131–139Cite as

Evolutionary Metaphor of Genetic Encoding for Self-organizable Robots

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10361))

Abstract

Computational models of multi-agent system are widely used for studying a variety of morphogenic processes, which include cell signaling, cell-cell interactions, pattern formation, and cell sorting during tissue self-assembly. This article describes a very simple genetic encoding and developmental system designed for self-organization of multi-cellular agents. The morphogenetic evolution system is guided by gene expression (genetic encoding) and cellular differentiation. Computer simulations show that the method can generate arbitrary 3D shape.

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Acknowledgments

This work was supported by a 2017 research grant from Youngsan University, Republic of Korea.

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Authors and Affiliations

  1. Department of Intelligent Robotics, Youngsan University, Yangsan, Korea

    Kiwon Yeom

Authors
  1. Kiwon Yeom
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Correspondence to Kiwon Yeom .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Politecnico di Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong, North Wollongong, New South Wales, Australia

    Prashan Premaratne

  4. Indian Institute of Technology, Kanpur, India

    Phalguni Gupta

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Yeom, K. (2017). Evolutionary Metaphor of Genetic Encoding for Self-organizable Robots. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10361. Springer, Cham. https://doi.org/10.1007/978-3-319-63309-1_13

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  • DOI: https://doi.org/10.1007/978-3-319-63309-1_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63308-4

  • Online ISBN: 978-3-319-63309-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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