On the Evolution of Homogeneous Multi-robot Teams: Clonal versus Aclonal Approach

  • Elio Tuci
  • Vito Trianni
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7426)


This study compares two different evolutionary approaches to the design of homogeneous multi-robot teams in a task that requires the agents to specialise in different roles. Our results diverge from what illustrated in a previous similar comparative study, which advocates for the superiority of the aclonal versus the clonal approach. We question this argument in view of new empirical evidence showing that the two approaches perform equally well in generating homogeneous teams.


Swarm Robotics Evolutionary Robotics 


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  1. 1.
    Ampatzis, C., Tuci, E., Trianni, V., Christensen, A.L., Dorigo, M.: Evolving self-assembly in autonomous homogeneous robots: Experiments with two physical robots. Artificial Life 15(4), 465–484 (2009)CrossRefGoogle Scholar
  2. 2.
    Beer, R.D., Gallagher, J.C.: Evolving dynamic neural networks for adaptive behavior. Adaptive Behavior 1(1), 91–122 (1992)CrossRefGoogle Scholar
  3. 3.
    Floreano, D., Mitri, S., Magnenat, S., Keller, L.: Evolutionary conditions for the emergence of communication in robots. Current Biology 17, 514–519 (2007)CrossRefGoogle Scholar
  4. 4.
    Jakobi, N.: Evolutionary robotics and the radical envelope of noise hypothesis. Adaptive Behavior 6, 325–368 (1997)CrossRefGoogle Scholar
  5. 5.
    Nolfi, S.: EvoRob 1.1 User Manual. Institute of Psychology, National Research Council (CNR) (2000), http://gral.ip.rm.cnr.it/evorobot/simulator.html
  6. 6.
    Nolfi, S., Floreano, D.: Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-organising Machine. MIT Press, Cambridge (2001)Google Scholar
  7. 7.
    Parker, L.: Springer Handbook of Robotics, ch. 40. Springer, Berlin (2008)Google Scholar
  8. 8.
    Quinn, M.: A comparison of approaches to the evolution of homogeneous multi/robot teams. In: Proceedings of the International Conference on Evolutionary Computation (CEC), vol. 1, pp. 128–135 (2001)Google Scholar
  9. 9.
    Trianni, V., Nolfi, S.: Engineering the evolution of self-organizing behaviors in swarm robotics: A case study. Artifcial Life 17(3), 183–202 (2011)CrossRefGoogle Scholar
  10. 10.
    Tuci, E.: An investigation of the evolutionary origin of reciprocal communication using simulated autonomous agents. Biological Cybernetics 101(3), 183–199 (2009)CrossRefGoogle Scholar
  11. 11.
    Waibel, M., Keller, L., Floreano, D.: Genetic team composition and level of selection in the evolution of cooperation. IEEE Transaction of Evolutionary Computation 13(3), 648–660 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Elio Tuci
    • 1
  • Vito Trianni
    • 2
  1. 1.Computer Science DepartmentAberystwyth UniversityAberystwythUK
  2. 2.Institute of Cognitive Science and TechnologyRomeItaly

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