Typed Functional Genetic and Monte Carlo Programming in Python.
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analyze_logs
imgs
stack_analyze
.gitignore
LICENSE
README.md
app_tree.py
cache.py
context.py
domain_fparity_apptree.py
domain_koza_apptree.py
domain_koza_stack.py
domain_parity_apptree.py
domain_parity_stack.py
domain_physics.py
domain_physics_smart.py
domain_primes_apptree.py
fitness_cache.py
generator.py
generator_static.py
i2c.py
i2c_domain.py
i2c_gen.py
i2c_nets.py
i2c_render.py
i2c_run.py
lispson.py
mcts.py
nmcs.py
normalization.py
parsers.py
run.sh
run_experiment.py
run_mc.py
stack.py
sub.py
test.sh
test_generator.py
test_lispson.py
test_mc_fparity.py
test_mc_koza.py
test_mc_parity.py
test_mc_primes.py
test_norm.py
test_normalization.py
test_sub.py
test_typ.py
test_utils.py
tracer_deco.py
tree_node.py
tree_stats.py
typ.py
typ_utils.py
utils.py

README.md

TFGPy

For instance:

./run_experiment.py --help
# or
./run_experiment.py --print-size-hist --repeat=1 --k=15 --mcts --mcts-num-steps=1000 --stack --domain=koza_poly