EvoLife

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

Requirements

• 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

Usage

$ python evolife.py [experiment_name]

or just

$ ./evolife.py [experiment_name]

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

Controls

• 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.

Experiments

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/bliamba.py, you have to run the following command: $ ./evolife.py 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/tutorial.py for further instructions.