Automatic Step Evolution

  • Tiago Baptista
  • Ernesto Costa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8864)


A common issue in Artificial Life research, and mainly in open-ended evolution simulations, is that of defining the bootstrap conditions of the simulations. One usual technique employed is the random initialization of individuals at the start of each simulation. However, by using this initialization method, we force the evolutionary process to always start from scratch, and thus require more time to accomplish the objective. Artificial Life simulations, being typically, very time consuming, suffer particularly when applying this method. In a previous paper we described a technique we call step evolution, analogous to incremental evolution techniques, that can be used to shorten the time needed to evolve complex behaviors in open-ended evolutionary simulations. In this paper we further extend this technique by automating the process of stepping the simulation. We provide results from experiments done on an open-ended evolution of foraging scenario, where agents evolve, adapting to a world with a day and night cycle. The results show that we can indeed automate this process and achieve a performance at least as good as on the best performant non-automated version.


Artificial life Multi-agent systems Open-ended evolution Incremental evolution 


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  1. 1.
    Baptista, T.: Complexity and Emergence in Societies of Agents. Ph.D. thesis, University of Coimbra, Coimbra (July 2012)Google Scholar
  2. 2.
    Baptista, T., Costa, E.: Step Evolution: Improving the Performance of Open-Ended Evolution Simulations. In: 2013 IEEE Symposium on Artificial Life (ALIFE), pp. 52–59 (2013)Google Scholar
  3. 3.
    Channon, A.: Three evolvability requirements for open-ended evolution. In: Maley, C.C., Boudreau, E. (eds.) Artificial Life VII Workshop Proceedings, Portland, OR, pp. 39–40 (2000)Google Scholar
  4. 4.
    Channon, A.: Evolutionary Emergence: The Struggle for Existence in Artificial Biota. Ph.D. thesis, University of Southampton (November 2001)Google Scholar
  5. 5.
    Eiben, A., Griffioen, A.R., Haasdijk, E.: Population-based Adaptive Systems: an Implementation in NEWTIES. In: ECCS 2007: European Conference on Complex Systems, Dresden, Germany (July 2007)Google Scholar
  6. 6.
    Gomez, F., Miikkulainen, R.: Incremental evolution of complex general behavior. Adaptive Behavior 5(3–4), 317–342 (1997)CrossRefGoogle Scholar
  7. 7.
    Maley, C.: Four steps toward open-ended evolution. In: GECCO 1999: Proceedings of the Genetic and ... (1999)Google Scholar
  8. 8.
    Miller, J.F. (ed.): Cartesian Genetic Programming. Natural Computing Series, 1st edn. Springer (September 2011)Google Scholar
  9. 9.
    Mouret, J., Doncieux, S.: Overcoming the bootstrap problem in evolutionary robotics using behavioral diversity. In: IEEE Congress on Evolutionary Computation, CEC 2009, pp. 1161–1168 (2009)Google Scholar
  10. 10.
    Nelson, A.L., Barlow, G.J., Doitsidis, L.: Fitness functions in evolutionary robotics: A survey and analysis. Robotics and Autonomous Systems 57(4), 345–370 (2009)CrossRefGoogle Scholar
  11. 11.
    Ray, T.S.: Evolution, Ecology and Optimization of Digital Organisms. Tech. Rep. 92–08-042, Santa Fe Institute (1992)Google Scholar
  12. 12.
    Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 2nd edn. Prentice Hall (December 2002)Google Scholar
  13. 13.
    Smith, J.M., Szathmáry, E.: The Major Transitions in Evolution. Oxford University Press, Oxford (1985)Google Scholar
  14. 14.
    Standish, R.K.: Open-ended artificial evolution. Int. J. Comput. Intell. Appl. 3(2), 167–175 (2003)CrossRefGoogle Scholar
  15. 15.
    Stanton, A., Channon, A.: Heterogeneous complexification strategies robustly outperform homogeneous strategies for incremental evolution. In: Proceedings of the Twelfth European Conference on the Synthesis and Simulation of Living Systems, pp. 973–980. MIT Press, Taormina (2013)Google Scholar
  16. 16.
    Stone, P., Veloso, M.M.: Layered Learning. In: Lopez de Mantaras, R., Plaza, E. (eds.) ECML 2000. LNCS (LNAI), vol. 1810, pp. 369–381. Springer, Heidelberg (2000)Google Scholar
  17. 17.
    Wu, A.S., Yu, H., Jin, S., Lin, K.C., Schiavone, G.: An Incremental Genetic Algorithm Approach to Multiprocessor Scheduling. IEEE Transactions on Parallel and Distributed Systems 15(9) (September 2004)Google Scholar
  18. 18.
    Yaeger, L.: Computational genetics, physiology, metabolism, neural systems, learning, vision, and behavior or Poly World: Life in a new context. In: Langton, C.G. (ed.) Artificial Life III: Proceedings of the Workshop on Artificial Life, pp. 263–298. Addison-Wesley (1994)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.CISUC, Department of Informatics EngineeringUniversity of CoimbraCoimbraPortugal

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