def fitness(self, net, proc, id_for_printing=-1):
total_weights = net.num_edges()
total_delays = net.num_edges()
total_thresholds = net.num_nodes()
dec = [8] * total_weights + [4] * total_delays + [7] * total_thresholds
leap_decoder = BinaryToIntDecoder(*dec)
problem = NetworkProblem(net, proc, self)
genome_len = 8 * total_weights + 4 * total_delays + 7 * total_thresholds
parents = Individual.create_population(
10,
initialize=create_binary_sequence(genome_len),
decoder=leap_decoder,
problem=problem)
parents = Individual.evaluate_population(parents)
max_generation = 10
#stdout_probe = probe.FitnessStatsCSVProbe(context, stream=sys.stdout)
generation_counter = util.inc_generation(context=context)
while generation_counter.generation() < max_generation:
offspring = pipe(
parents, ops.tournament_selection, ops.clone,
mutate_bitflip, ops.uniform_crossover, ops.evaluate,
ops.pool(size=len(parents))) #, # accumulate offspring
#stdout_probe)
parents = offspring
generation_counter() # increment to the next generation
best = probe.best_of_gen(parents).decode()
set_weights(best, net)
return self.get_fitness_score(net, proc, id_for_printing)
"""
from toolz import pipe
from leap_ec.individual import Individual
from leap_ec.decoder import IdentityDecoder
from leap_ec.global_vars import context
import leap_ec.ops as ops
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
from leap_ec import util
# create initial rand population of 5 individuals
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)
# generation_counter is an optional convenience for generation tracking
generation_counter = util.inc_generation(context=context)
while generation_counter.generation() < 6:
offspring = pipe(parents, ops.tournament_selection, ops.clone,
mutate_bitflip(expected_num_mutations=1),
ops.uniform_crossover(p_swap=0.2), ops.evaluate,
ops.pool(size=len(parents))) # accumulate offspring
# 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
# print(f"{i}, {best}")
def init(length, seq_initializer):
def create():
return initializers.create_segmented_sequence(
gene_size, create_int_vector(bounds))
return create
#create initial rand population of 5 individuals
parents = Individual.create_population(n=pop_size,
initialize=init(
gene_size,
create_int_vector(bounds)),
decoder=decoder.IdentityDecoder(),
problem=FunctionProblem(p.f, True))
# Evaluate initial population
parents = Individual.evaluate_population(parents)
# print initial, random population
util.print_population(parents, generation=0)
# generation_counter is an optional convenience for generation tracking
generation_counter = util.inc_generation(context=context)
#results = []
while generation_counter.generation() < 20:
p.setStat()