Morphological Attractors in Darwinian and Lamarckian Evolutionary Robot Systems

Milan Jelisavcic; Karine Miras; A. E. Eiben

doi:10.1109/SSCI.2018.8628844

Morphological Attractors in Darwinian and Lamarckian Evolutionary Robot Systems

Milan Jelisavcic, Karine Miras, A. E. Eiben

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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Abstract

Morphological evolution in a robotic system produces novel robot bodies after each reproduction event. This implies the necessity for life-time learning so that newborn robots can acquire a controller that fits their body. Thus, we obtain a system where evolution and learning are combined. This combination can be Darwinian or Lamarckian and in this paper, we compare the two. In particular, we investigate the evolved morphologies under these regimes for modular robots evolved for good locomotion. Using eight quantifiable morphological descriptors to characterize the physical properties of robots we compare the regions of attraction in the resulting 8-dimensional space. The results show prominent differences in symmetry, size, proportion, and coverage.

Original language	English
Title of host publication	2018 IEEE Symposium Series on Computational Intelligence (SSCI)
Subtitle of host publication	[Proceedings]
Editors	Suresh Sundaram
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	859-866
Number of pages	8
ISBN (Electronic)	9781538692769
ISBN (Print)	9781538692776
DOIs	https://doi.org/10.1109/SSCI.2018.8628844
Publication status	Published - 2019
Event	8th IEEE Symposium Series on Computational Intelligence, SSCI 2018 - Bangalore, India Duration: 18 Nov 2018 → 21 Nov 2018

Conference

Conference	8th IEEE Symposium Series on Computational Intelligence, SSCI 2018
Country/Territory	India
City	Bangalore
Period	18/11/18 → 21/11/18

Keywords

Lamarckian evolution
Modular robots
Online learning
Embodied evolution
Artificial life
Evolutionary robotics

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10.1109/SSCI.2018.8628844

SS-1226Final published version, 478 KBLicence: Article 25fa Dutch Copyright Act

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title = "Morphological Attractors in Darwinian and Lamarckian Evolutionary Robot Systems",

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booktitle = "2018 IEEE Symposium Series on Computational Intelligence (SSCI)",

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note = "8th IEEE Symposium Series on Computational Intelligence, SSCI 2018 ; Conference date: 18-11-2018 Through 21-11-2018",

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Jelisavcic, M, Miras, K & Eiben, AE 2019, Morphological Attractors in Darwinian and Lamarckian Evolutionary Robot Systems. in S Sundaram (ed.), 2018 IEEE Symposium Series on Computational Intelligence (SSCI) : [Proceedings]., 8628844, Institute of Electrical and Electronics Engineers Inc., pp. 859-866, 8th IEEE Symposium Series on Computational Intelligence, SSCI 2018, Bangalore, India, 18/11/18. https://doi.org/10.1109/SSCI.2018.8628844

Morphological Attractors in Darwinian and Lamarckian Evolutionary Robot Systems. / Jelisavcic, Milan; Miras, Karine ; Eiben, A. E.
2018 IEEE Symposium Series on Computational Intelligence (SSCI) : [Proceedings]. ed. / Suresh Sundaram. Institute of Electrical and Electronics Engineers Inc., 2019. p. 859-866 8628844.

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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N2 - Morphological evolution in a robotic system produces novel robot bodies after each reproduction event. This implies the necessity for life-time learning so that newborn robots can acquire a controller that fits their body. Thus, we obtain a system where evolution and learning are combined. This combination can be Darwinian or Lamarckian and in this paper, we compare the two. In particular, we investigate the evolved morphologies under these regimes for modular robots evolved for good locomotion. Using eight quantifiable morphological descriptors to characterize the physical properties of robots we compare the regions of attraction in the resulting 8-dimensional space. The results show prominent differences in symmetry, size, proportion, and coverage.

AB - Morphological evolution in a robotic system produces novel robot bodies after each reproduction event. This implies the necessity for life-time learning so that newborn robots can acquire a controller that fits their body. Thus, we obtain a system where evolution and learning are combined. This combination can be Darwinian or Lamarckian and in this paper, we compare the two. In particular, we investigate the evolved morphologies under these regimes for modular robots evolved for good locomotion. Using eight quantifiable morphological descriptors to characterize the physical properties of robots we compare the regions of attraction in the resulting 8-dimensional space. The results show prominent differences in symmetry, size, proportion, and coverage.

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Morphological Attractors in Darwinian and Lamarckian Evolutionary Robot Systems

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