from toolz import pipe
from leap_ec import Individual, context, test_env_var
from leap_ec import ops, probe, util
from leap_ec.decoder import IdentityDecoder
from leap_ec.binary_rep.problems import MaxOnes
from leap_ec.binary_rep.initializers import create_binary_sequence
from leap_ec.binary_rep.ops import mutate_bitflip
##############################
# Entry point
##############################
if __name__ == '__main__':
parents = Individual.create_population(
5,
initialize=create_binary_sequence(4),
decoder=IdentityDecoder(),
problem=MaxOnes())
# Evaluate initial population
parents = Individual.evaluate_population(parents)
# print initial, random population
util.print_population(parents, generation=0)
# When running the test harness, just run for two generations
# (we use this to quickly ensure our examples don't get bitrot)
if os.environ.get(test_env_var, False) == 'True':
max_generation = 2
else:
max_generation = 6
# this was in the context of the 1/5 success rule, which we've not
# implemented here.
# Handbook of EC, B1.3:2
context['leap']['std'] *= .85
if __name__ == '__main__':
# Define the real value bounds for initializing the population. In this case,
# we define a genome of four bounds.
# the (-5.12,5.12) was what was originally used for this problem in
# Ken De Jong's 1975 dissertation, so was used for historical reasons.
bounds = [(-5.12, 5.12), (-5.12, 5.12), (-5.12, 5.12), (-5.12, 5.12)]
parents = Individual.create_population(
5,
initialize=create_real_vector(bounds),
decoder=IdentityDecoder(),
problem=SpheroidProblem(maximize=False))
# Evaluate initial population
parents = Individual.evaluate_population(parents)
context['leap']['std'] = 2
# We use the provided context, but we could roll our own if we
# wanted to keep separate contexts. E.g., island models may want to have
# their own contexts.
generation_counter = util.inc_generation(context=context,
callbacks=(anneal_std, ))
# print initial, random population