Search Space Analysis of Evolvable Robot Morphologies

  • Karine MirasEmail author
  • Evert Haasdijk
  • Kyrre Glette
  • A. E. Eiben
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10784)


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.


Modular robots Evolutionary Robotics Morphology Generative encoding Novelty search 



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


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Karine Miras
    • 1
    Email author
  • Evert Haasdijk
    • 1
  • Kyrre Glette
    • 2
  • A. E. Eiben
    • 1
  1. 1.Vrije Universiteit AmsterdamAmsterdamThe Netherlands
  2. 2.University of OsloOsloNorway

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