The Evolution of Foraging in an Open-Ended Simulation Environment

  • Tiago Baptista
  • Ernesto Costa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7026)


Throughout the last decades, Darwin’s theory of natural selection has fueled a vast amount of research in the field of computer science, and more specifically in artificial intelligence. The majority of this work has focussed on artificial selection, rather than on natural selection. In this paper we study the evolution of agents’ controllers in an open-ended scenario. To that end, we set up a multi-agent simulation inspired by the ant foraging task, and evolve the agents’ brain (a rule list) without any explicit fitness function. We show that the agents do evolve sustainable foraging behaviors in this environment, and discuss some evolutionary conditions that seem to be important to achieve these results.


artificial life open-ended evolution multi-agent ant foraging 


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  1. 1.
    Baptista, T., Costa, E.: Evolution of a multi-agent system in a cyclical environment. Theory in Biosciences 127(2), 141–148 (2008)CrossRefGoogle Scholar
  2. 2.
    Baptista, T., Menezes, T., Costa, E.: Bitbang: A model and framework for complexity research. In: Proceedings of the European Conference on Complex Systems (September 2006)Google Scholar
  3. 3.
    Bonabeau, E., Theraulaz, G., Dorigo, M.: Swarm Intelligence: From Natural to Artificial Systems, 1st edn. Oxford University Press, USA (1999)zbMATHGoogle Scholar
  4. 4.
    Channon, A.: Three evolvability requirements for open-ended evolution. In: Artificial Life VII Workshop Proceedings (2000)Google Scholar
  5. 5.
    Collins, R.J., Jefferson, D.R.: AntFarm: Towards Simulated Evolution. In: Artificial Life II, University of California, pp. 1–23 (May 1991)Google Scholar
  6. 6.
    Dorigo, M., Stutzle, T.: Ant Colony Optimization (Bradford Books). The MIT Press, Cambridge (2004)zbMATHGoogle Scholar
  7. 7.
    Jablonda, E., Lamb, M.: Evolution in Four Dimensions: Genetic, Epigenetic, Behavioral, and Symbolic Variation in the History of Life. MIT Press, MA (2006)Google Scholar
  8. 8.
    Kawamura, H., Ohuchi, A.: Evolutionary Emergence of Collective Intelligence with Artificial Pheromone Communication. In: IEEE International Conference on Industrial Electronics, Control and Instrumentation, pp. 2831–2836. IEEE, Graduate School of Eng. (2000)Google Scholar
  9. 9.
    Nakamichi, Y.: Effectiveness of emerged pheromone communication in an ant foraging model. In: Proceedings of the Tenth International Symposium... (2005)Google Scholar
  10. 10.
    Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 2nd edn. Prentice Hall, Englewood Cliffs (2002)zbMATHGoogle Scholar
  11. 11.
    Yaeger, L.: Computational genetics, physiology, metabolism, neural systems, learning, vision, and behavior or Poly World: Life in a new context. In: Proceedings of the Artificial Life III Conference, pp. 263–298 (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Tiago Baptista
    • 1
  • Ernesto Costa
    • 1
  1. 1.CISUC, Department of Informatics EngineeringUniversity of CoimbraCoimbraPortugal

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