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Search Space Analysis of Evolvable Robot Morphologies

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Applications of Evolutionary Computation (EvoApplications 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10784))

Abstract

We present a study on morphological traits of evolved modular robots. We note that the evolutionary search space –the set of obtainable morphologies– depends on the given representation and reproduction operators and we propose a framework to assess morphological traits in this search space regardless of a specific environment and/or task. To this end, we present eight quantifiable morphological descriptors and a generic novelty search algorithm to produce a diverse set of morphologies for any given representation. With this machinery, we perform a comparison between a direct encoding and a generative encoding. The results demonstrate that our framework permits to find a very diverse set of bodies, allowing a morphological diversity investigation. Furthermore, the analysis showed that despite the high levels of diversity, a bias to certain traits in the population was detected. Surprisingly, the two encoding methods showed no significant difference in the diversity levels of the evolved morphologies or their morphological traits.

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Notes

  1. 1.

    http://robogen.org/docs/robot-body-parts/.

  2. 2.

    https://tinyurl.com/y9s8ssuc.

  3. 3.

    The types of modules would not have to be necessarily the same, as long as the body had the same amount of modules.

  4. 4.

    https://tinyurl.com/yc364pfe.

  5. 5.

    https://tinyurl.com/ybpcvdqp.

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Acknowledgements

This project has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No. 665347.

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Authors and Affiliations

  1. Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Karine Miras, Evert Haasdijk & A. E. Eiben

  2. University of Oslo, Oslo, Norway

    Kyrre Glette

Authors
  1. Karine Miras
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  2. Evert Haasdijk
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  3. Kyrre Glette
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  4. A. E. Eiben
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Corresponding author

Correspondence to Karine Miras .

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Editors and Affiliations

  1. Edinburgh Napier University, Edinburgh, United Kingdom

    Kevin Sim

  2. Johannes Gutenberg University of Mainz, Mainz, Germany

    Paul Kaufmann

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Miras, K., Haasdijk, E., Glette, K., Eiben, A.E. (2018). Search Space Analysis of Evolvable Robot Morphologies. In: Sim, K., Kaufmann, P. (eds) Applications of Evolutionary Computation. EvoApplications 2018. Lecture Notes in Computer Science(), vol 10784. Springer, Cham. https://doi.org/10.1007/978-3-319-77538-8_47

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  • DOI: https://doi.org/10.1007/978-3-319-77538-8_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77537-1

  • Online ISBN: 978-3-319-77538-8

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