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Life-like cellular automaton with evolutionary rules for each cell.


  • Python 2.7
  • NumPy / SciPy
  • PyCUDA
  • PyGame
  • scikit-image
  • NVidia CUDA Toolkit
  • Powerful NVidia GPU is recommended, but should work with any CUDA enabled card

If you're using a Debian-like distro:

$ sudo apt-get install python-pycuda python-numpy python-scipy python-pygame nvidia-cuda-toolkit python-setuptools

$ sudo easy_install scikit-image


$ python [experiment_name]

or just

$ ./ [experiment_name]

If no preset given, default 'big bang' is used.


  • Arrows: move field around
  • + / -: zoom field in/out
  • ] / [: increase/decrease frame skip
  • F: toggle fullscreen
  • S: save a field dump to fields/field.npy file
  • Q / ESC: quit

Every 100 steps, top 10 species will be printed to a console. SN is a total number of species currently on the board.

Automaton Rules

  • Each living cell has its own birth/sustain ruleset and an energy level;
  • Cell is loosing all energy if number of neighbours is not in its sustain rule;
  • Cell is born with max energy if there are exactly N neighbours with N in their birth rule;
    • Same is applied for living cells (re-occupation case), if new genome is different;
  • If there are several birth situations with different N possible, we choose one with larger N;
  • Newly born cell's ruleset calculated as crossover between 'parent' cells rulesets;
  • If cell is involved in breeding as a 'parent', it's loosing BIRTH_COST units of energy per each non-zero gene passed;
    • This doesn't apply in re-occupation case;
  • Every turn, cell is loosing DEATH_SPEED units of energy;
  • Cell with zero energy is dying;
  • Cell cannot have more than MAX_GENES non-zero genes in ruleset.


You may see a list of experimental presets in experiments folder. To run a particular experiment, provide an experiment's filename without .py extension. For example to run an experiment described in experiments/, you have to run the following command: $ ./ bliamba

Most of the provided experiments are set without fixed random seed. Run each of them several times, they could show different behaviours.

If you are familiar with Python / NumPy, you can easily set up your own experiment. See experiments/ for further instructions.


Life-like cellular automaton with evolutionary rules for each cell.




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