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

Hart, Emma; Steyven, Andreas S. W.; Paechter, Ben



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.

Presentation Conference Type Conference Paper (Published)
Conference Name GECCO 2018
Start Date Jul 15, 2018
End Date Jul 19, 2018
Acceptance Date Mar 24, 2018
Online Publication Date Jul 2, 2018
Publication Date Jul 2, 2018
Deposit Date Apr 3, 2018
Publicly Available Date Apr 20, 2018
Publisher Association for Computing Machinery (ACM)
Pages 101-108
Book Title GECCO '18 Proceedings of the Genetic and Evolutionary Computation Conference
ISBN 978-1-4503-5618-3
Keywords Computing methodologies, Cooperation and coordination,
Public URL
Contract Date Apr 20, 2018


Evolution of a Functionally Diverse Swarm via a Novel Decentralised Quality-Diversity Algorithm (1 Mb)

Copyright Statement
© 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,

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