Evolving the Game of Life

  • Dimitar Kazakov
  • Matthew Sweet
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3394)


It is difficult to define a set of rules for a cellular automaton (CA) such that creatures with life-like properties (stability and dynamic behaviour, reproducton and self-repair) can be grown from a large number of initial configurations. This work describes an evolutionary framework for the search of a CA with these properties. Instead of encoding them directly into the fitness function, we propose one, which maximises the variance of entropy across the CA grid. This fitness function promotes the existence of areas on the verge of chaos, where life is expected to thrive. The results are reported for the case of CA in which cells are in one of four possible states. We also describe a mechanism for fitness sharing that successfully speeds up the genetic search, both in terms of number of generations and CPU time.


Genetic Algorithm Cellular Automaton Inductive Logic Programming Game Board Tile Size 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Dimitar Kazakov
    • 1
  • Matthew Sweet
    • 1
  1. 1.Department of Computer ScienceUniversity of YorkHeslington, YorkUK

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