Leveraging Online Racing and Population Cloning in Evolutionary Multirobot Systems

  • Fernando Silva
  • Luís Correia
  • Anders Lyhne Christensen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9598)

Abstract

Online evolution of controllers on real robots typically requires a prohibitively long time to synthesise effective solutions. In this paper, we introduce two novel approaches to accelerate online evolution in multirobot systems. We introduce a racing technique to cut short the evaluation of poor controllers based on the task performance of past controllers, and a population cloning technique that enables individual robots to transmit an internal set of high-performing controllers to robots nearby. We implement our approaches over odNEAT, which evolves artificial neural network controllers. We assess the performance of our approaches in three tasks involving groups of e-puck-like robots, and we show that they facilitate: (i) controllers with higher performance, (ii) faster evolution in terms of wall-clock time, (iii) more consistent group-level performance, and (iv) more robust, well-adapted controllers.

Keywords

Online evolution Multirobot systems Racing Population cloning 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Fernando Silva
    • 1
    • 2
    • 4
  • Luís Correia
    • 4
  • Anders Lyhne Christensen
    • 1
    • 2
    • 3
  1. 1.BioMachines LabLisboaPortugal
  2. 2.Instituto de TelecomunicaçõesLisboaPortugal
  3. 3.Instituto Universitário de Lisboa (ISCTE-IUL)LisboaPortugal
  4. 4.BioISI, Faculdade de CiênciasUniversidade de LisboaLisboaPortugal

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