Life-like cellular automaton with evolutionary rules for each cell.
- Python 2.7
- NumPy / SciPy
- 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 evolife.py [experiment_name]
$ ./evolife.py [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
- 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.
- 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_COSTunits of energy per each non-zero gene passed;
- This doesn't apply in re-occupation case;
- Every turn, cell is loosing
DEATH_SPEEDunits of energy;
- Cell with zero energy is dying;
- Cell cannot have more than
MAX_GENESnon-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/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.