Leveraging Online Racing and Population Cloning in Evolutionary Multirobot Systems

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


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.


Online evolution Multirobot systems Racing Population cloning 



This work was partly supported by FCT under grants SFRH/BD/89573/2012, UID/EEA/50008/2013, and UID/Multi/04046/2013.


  1. 1.
    Floreano, D., Mondada, F.: Automatic creation of an autonomous agent: genetic evolution of a neural-network driven robot. In: 3rd International Conference on Simulation of Adaptive Behavior, pp. 421–430. MIT Press, Cambridge (1994)Google Scholar
  2. 2.
    Watson, R., Ficici, S., Pollack, J.: Embodied evolution: distributing an evolutionary algorithm in a population of robots. Rob. Auton. Syst. 39(1), 1–18 (2002)CrossRefGoogle Scholar
  3. 3.
    Silva, F., Urbano, P., Correia, L., Christensen, A.L.: odNEAT: an algorithm for decentralised online evolution of robotic controllers. Evol. Comput. 23(3), 421–449 (2015)CrossRefGoogle Scholar
  4. 4.
    Silva, F., Correia, L., Christensen, A.L.: A case study on the scalability of online evolution of robotic controllers. In: Pereira, F., Machado, P., Costa, E., Cardoso, A. (eds.) EPIA 2015. LNCS, vol. 9273, pp. 189–200. Springer, Heidelberg (2015)Google Scholar
  5. 5.
    Haasdijk, E., Eiben, A., Karafotias, G.: On-line evolution of robot controllers by an encapsulated evolution strategy. In: IEEE Congress on Evolutionary Computation, pp. 1–7. IEEE Press, Piscataway (2010)Google Scholar
  6. 6.
    Prieto, A., Becerra, J., Bellas, F., Duro, R.J.: Open-ended evolution as a means to self-organize heterogeneous multi-robot systems in real time. Rob. Auton. Syst. 58(12), 1282–1291 (2010)CrossRefGoogle Scholar
  7. 7.
    Bredeche, N., Montanier, J.M., Liu, W., Winfield, A.: Environment-driven distributed evolutionary adaptation in a population of autonomous robotic agents. Math. Comput. Model. Dyn. Syst. 18(1), 101–129 (2012)CrossRefzbMATHGoogle Scholar
  8. 8.
    Silva, F., Duarte, M., Correia, L., Oliveira, S.M., Christensen, A.L.: Open issues in evolutionary robotics. Evol. Comput. In press (2016).
  9. 9.
    Dinu, C.M., Dimitrov, P., Weel, B., Eiben, A.: Self-adapting fitness evaluation times for on-line evolution of simulated robots. In: 15th Genetic and Evolutionary Computation Conference, pp. 191–198. ACM, New York (2013)Google Scholar
  10. 10.
    Arif, A., Nedev, D., Haasdijk, E.: Controlling maximum evaluation duration in on-line and on-board evolutionary robotics. Evolving Syst. 5(4), 275–286 (2014)CrossRefGoogle Scholar
  11. 11.
    Haasdijk, E., Atta-ul Qayyum, A., Eiben, A.: Racing to improve on-line, on-board evolutionary robotics. In: 13th Genetic and Evolutionary Computation Conference, pp. 187–194. ACM, New York (2011)Google Scholar
  12. 12.
    Haasdijk, E., Smit, S.K., Eiben, A.E.: Exploratory analysis of an on-line evolutionary algorithm in simulated robots. Evol. Intell. 5(4), 213–230 (2012)CrossRefGoogle Scholar
  13. 13.
    Hoeffding, W.: Probability inequalities for sums of bounded random variables. J. Am. Stat. Assoc. 58(301), 13–30 (1963)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Silva, F., Correia, L., Christensen, A.L.: Dynamics of neuronal models in online neuroevolution of robotic controllers. In: Correia, L., Reis, L.P., Cascalho, J. (eds.) EPIA 2013. LNCS, vol. 8154, pp. 90–101. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  15. 15.
    Silva, F., Urbano, P., Christensen, A.L.: Online evolution of adaptive robot behaviour. Int. J. Nat. Comput. Res. 4(2), 59–77 (2014)CrossRefGoogle Scholar
  16. 16.
    Mondada, F., Bonani, M., Raemy, X., Pugh, J., Cianci, C., Klaptocz, A., Magnenat, S., Zufferey, J., Floreano, D., Martinoli, A.: The e-puck, a robot designed for education in engineering. In: 9th Conference on Autonomous Robot Systems and Competitions, IPCB, Castelo Branco, Portugal, pp. 59–65 (2009)Google Scholar
  17. 17.
    Maron, O., Moore, A.W.: The racing algorithm: model selection for lazy learners. Artif. Intell. Rev. 11(1), 193–225 (1997)CrossRefGoogle Scholar
  18. 18.
    Lobo, F.G.: The Parameter-Less Genetic Algorithm: Rational and Automated Parameter Selection for Simplified Genetic Algorithm Operation. Ph.D. thesis, Universidade Nova de Lisboa, Lisbon, Portugal (2000)Google Scholar
  19. 19.
    Birattari, M., Stützle, T., Paquete, L., Varrentrapp, K.: A racing algorithm for configuring metaheuristics. In: 4th Genetic and Evolutionary Computation Conference, pp. 11–18. Morgan Kauffmann, San Francisco (2002)Google Scholar
  20. 20.
    Yuan, B., Gallagher, M.: Combining meta-EAs and racing for difficult EA parameter tuning tasks. In: Lob, F.G., Lima, F.C., Michalewicz, Z. (eds.) Parameter Setting in Evolutionary Algorithms. Studies in Computational Intelligence, vol. 54, pp. 121–142. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  21. 21.
    Silva, F., Correia, L., Christensen, A.L.: Speeding up online evolution of roboticcontrollers with macro-neurons. In: Esparcia-Alcázar, A.I., Mora, A.M. (eds.) EvoApplications 2014. LNCS, vol. 8602, pp. 765–776. Springer, Heidelberg (2014)Google Scholar
  22. 22.
    Duarte, M., Silva, F., Rodrigues, T., Oliveira, S.M., Christensen, A.L.: JBotEvolver: a versatile simulation platform for evolutionary robotics. In: 14th International Conference on the Synthesis and Simulation of Living Systems, pp. 210–211. MIT Press, Cambridge (2014)Google Scholar
  23. 23.
    Cao, Y., Fukunaga, A., Kahng, A.: Cooperative mobile robotics: antecedents and directions. Auton. Rob. 4(1), 1–23 (1997)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Fernando Silva
    • 1
    • 2
    • 4
    Email author
  • 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

Personalised recommendations