Research Output

Evolution of a Functionally Diverse Swarm via a Novel Decentralised Quality-Diversity Algorithm

  The presence of functionality diversity within a group has been demonstrated to lead to greater robustness, higher performance and increased problem-solving ability in a broad range of studies that includes insect groups, human groups and swarm robotics. Evolving group diversity however has proved challenging within Evolutionary Robotics, requiring reproductive isolation and careful attention to population size and selection mechanisms. To tackle this issue, we introduce a novel, decentralised, variant of the MAP-Elites illumination algorithm which is hybridised with a well-known distributed evolutionary algorithm (mEDEA). The algorithm simultaneously evolves multiple diverse behaviours for multiple robots, with respect to a simple token-gathering task. Each robot in the swarm maintains a local archive defined by two pre-specified functional traits which is shared with robots it come into contact with. We investigate four different strategies for sharing, exploiting and combining local archives and compare results to mEDEA. Experimental results show that in contrast to previous claims, it is possible to evolve a functionally diverse swarm without geographical isolation , and that the new method outperforms mEDEA in terms of the diversity, coverage and precision of the evolved swarm.

  • Date:

    24 March 2018

  • Publication Status:

    Accepted

  • Publisher

    Association for Computing Machinery

  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    004 Data processing & computer science

  • Funders:

    Edinburgh Napier Funded

Citation

Hart, E., Steyven, A. S. W., & Paechter, B. (in press). Evolution of a Functionally Diverse Swarm via a Novel Decentralised Quality-Diversity Algorithm. In Proceedings of GECCO 2018

Authors

Copyright

© 2018 Association for Computing Machinery.
This is the author’s version of the work. It is posted here for your personal use.
Not for redistribution. The definitive Version of Record was published in GECCO ’18: Genetic and Evolutionary Computation Conference, July 15–19, 2018, Kyoto, Japan,
https://doi.org/10.1145/3205455.3205481

Keywords

Computing methodologies, Cooperation and coordination,

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