Genetic Programming and Evolvable Machines

, Volume 9, Issue 4, pp 281–294 | Cite as

Evolving strategy for a probabilistic game of imperfect information using genetic programming

  • Wojciech Jaśkowski
  • Krzysztof KrawiecEmail author
  • Bartosz Wieloch
Original Paper


We provide the complete record of methodology that let us evolve BrilliAnt, the winner of the Ant Wars contest. Ant Wars contestants are virtual ants collecting food on a grid board in the presence of a competing ant. BrilliAnt has been evolved through a competitive one-population coevolution using genetic programming and fitnessless selection. In this paper, we detail the evolutionary setup that lead to BrilliAnt’s emergence, assess its direct and indirect human-competitiveness, and describe the behavioral patterns observed in its strategy.


Genetic Programming Game Playing Board State Food Piece Popular Benchmark Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors wish to thank the anonymous reviewers for valuable feedback and discussion on this work. This research has been supported by the Ministry of Science and Higher Education grant # N N519 3505 33.


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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Wojciech Jaśkowski
    • 1
  • Krzysztof Krawiec
    • 1
    Email author
  • Bartosz Wieloch
    • 1
  1. 1.Institute of Computing SciencePoznan University of TechnologyPoznanPoland

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