def reproduce(self, generation, pop, sorted_species):
champ_done = False
mut_power = neat.weight_mut_power
total_fitness = 0.0
if self.expected_offspring > 0 and len(self.organisms) == 0:
return False
poolsize = len(self.organisms) - 1
thechamp_i = 0
for count in range(0, self.expected_offspring):
mut_struct_baby = False
mate_baby = False
outside = False
mom = dad = None
if self.organisms[thechamp_i].super_champ_offspring > 0:
dprint(DEBUG_CHECK, "super_champ_offspring()")
mom_i = thechamp_i
mom = self.organisms[mom_i]
new_genome = mom.gnome.duplicate(count)
if self.organisms[thechamp_i].super_champ_offspring == 1:
pass
if self.organisms[thechamp_i].super_champ_offspring > 1:
if neat.randfloat() < 0.8 or neat.mutate_add_link_prob == 0.0:
new_genome.mutate_link_weights(mut_power, 1.0, Genome.GAUSSIAN)
else:
net_analogue = new_genome.genesis(generation)
ok, pop.cur_innov_num = \
new_genome.mutate_add_link(pop.innovations, pop.cur_innov_num, neat.newlink_tries)
net_analogue = None
mut_struct_baby = True
#
baby = Organism()
baby.SetFromGenome(0.0, new_genome, generation)
if self.organisms[thechamp_i].super_champ_offspring == 1:
if self.organisms[thechamp_i].pop_champ:
baby.pop_champ_child = True
baby.high_fit = mom.orig_fitness
#
self.organisms[thechamp_i].super_champ_offspring -= 1
self.verify_baby(baby, mom, dad)
# super_champ_offspring > 0
elif (not champ_done) and (self.expected_offspring > 5):
dprint(DEBUG_CHECK, "champ()")
mom_i = thechamp_i
mom = self.organisms[mom_i]
new_genome = mom.gnome.duplicate(count)
baby = Organism()
baby.SetFromGenome(0.0, new_genome, generation)
champ_done = True
self.verify_baby(baby, mom, dad)
# (not champ_done) and (self.expected_offspring > 5)
elif (neat.randfloat() < neat.mutate_only_prob) or (poolsize == 0):
dprint(DEBUG_CHECK, "mutate_only()")
mom_i = neat.randint(0, poolsize)
mom = self.organisms[mom_i]
new_genome = mom.gnome.duplicate(count)
self.verify_genome(new_genome)
if neat.randfloat() < neat.mutate_add_node_prob:
dprint(DEBUG_INTEGRITY, "a: pop.cur_node_id:", pop.cur_node_id)
ok, pop.cur_node_id, pop.cur_innov_num = \
new_genome.mutate_add_node(pop.innovations, pop.cur_node_id, pop.cur_innov_num)
dprint(DEBUG_INTEGRITY, "b: pop.cur_node_id:", pop.cur_node_id)
mut_struct_baby = True
self.verify_genome(new_genome)
elif neat.randfloat() < neat.mutate_add_link_prob:
net_analogue = new_genome.genesis(generation)
ok, pop.cur_innov_num = \
new_genome.mutate_add_link(pop.innovations, pop.cur_innov_num, neat.newlink_tries)
net_analogue = None
mut_struct_baby = True
self.verify_genome(new_genome)
else:
if neat.randfloat() < neat.mutate_random_trait_prob:
new_genome.mutate_random_trait()
self.verify_genome(new_genome)
if neat.randfloat() < neat.mutate_link_trait_prob:
new_genome.mutate_link_trait(1)
self.verify_genome(new_genome)
if neat.randfloat() < neat.mutate_node_trait_prob:
new_genome.mutate_node_trait(1)
self.verify_genome(new_genome)
if neat.randfloat() < neat.mutate_link_weights_prob:
new_genome.mutate_link_weights(mut_power, 1.0, Genome.GAUSSIAN)
self.verify_genome(new_genome)
if neat.randfloat() < neat.mutate_toggle_enable_prob:
new_genome.mutate_toggle_enable(1)
self.verify_genome(new_genome)
if neat.randfloat() < neat.mutate_gene_reenable_prob:
new_genome.mutate_gene_reenable()
self.verify_genome(new_genome)
baby = Organism()
baby.SetFromGenome(0.0, new_genome, generation)
self.verify_baby(baby, mom, dad)
# (neat.randfloat() < neat.mutate_only_prob) or (poolsize == 0)
else:
mom_i = neat.randint(0, poolsize)
mom = self.organisms[mom_i]
if neat.randfloat() > neat.interspecies_mate_rate:
# within species
dad_i = neat.randint(0, poolsize)
dad = self.organisms[dad_i]
else:
# outside species
randspecies = self
giveup = 0
while (randspecies == self) and (giveup < 5):
randmult = neat.gaussrand() / 4.
if randmult > 1.0:
randmult = 1.0
randspeciesnum = int(math.floor( (randmult * (len(sorted_species) - 1.0)) + 0.5 ))
randspecies = sorted_species[randspeciesnum]
giveup += 1
dad = randspecies.organisms[0]
outside = True
if neat.randfloat() < neat.mate_multipoint_prob:
dprint(DEBUG_CHECK, "mate_multipoint()")
new_genome = mom.gnome.mate_multipoint(dad.gnome, count, mom.orig_fitness, dad.orig_fitness, outside)
elif neat.randfloat() < neat.mate_multipoint_avg_prob / (neat.mate_multipoint_avg_prob+neat.mate_singlepoint_prob):
dprint(DEBUG_CHECK, "mate_multipoint_avg()")
new_genome = mom.gnome.mate_multipoint_avg(dad.gnome, count, mom.orig_fitness, dad.orig_fitness, outside)
else:
dprint(DEBUG_CHECK, "mate_singlepoint()")
new_genome = mom.gnome.mate_singlepoint(dad.gnome, count)
mate_baby = True
if ((neat.randfloat() > neat.mate_only_prob) or
(dad.gnome.genome_id == mom.gnome.genome_id) or
(dad.gnome.compatibility(mom.gnome) == 0.0)):
dprint(DEBUG_CHECK, "mate_and_mutate()")
if neat.randfloat() < neat.mutate_add_node_prob:
dprint(DEBUG_INTEGRITY, "a: pop.cur_node_id:", pop.cur_node_id)
ok, pop.cur_node_id, pop.cur_innov_num = \
new_genome.mutate_add_node(pop.innovations, pop.cur_node_id, pop.cur_innov_num)
dprint(DEBUG_INTEGRITY, "b: pop.cur_node_id:", pop.cur_node_id)
mut_struct_baby = True
elif neat.randfloat() < neat.mutate_add_link_prob:
net_analogue = new_genome.genesis(generation)
ok, pop.cur_innov_num = \
new_genome.mutate_add_link(pop.innovations, pop.cur_innov_num, neat.newlink_tries)
net_analogue = None
mut_struct_baby = True
else:
if neat.randfloat() < neat.mutate_random_trait_prob:
new_genome.mutate_random_trait()
if neat.randfloat() < neat.mutate_link_trait_prob:
new_genome.mutate_link_trait(1)
if neat.randfloat() < neat.mutate_node_trait_prob:
new_genome.mutate_node_trait(1)
if neat.randfloat() < neat.mutate_link_weights_prob:
new_genome.mutate_link_weights(mut_power, 1.0, Genome.GAUSSIAN)
if neat.randfloat() < neat.mutate_toggle_enable_prob:
new_genome.mutate_toggle_enable(1)
if neat.randfloat() < neat.mutate_gene_reenable_prob:
new_genome.mutate_gene_reenable()
baby = Organism()
baby.SetFromGenome(0.0, new_genome, generation)
self.verify_baby(baby, mom, dad)
else:
dprint(DEBUG_CHECK, "mate_only()")
baby = Organism()
baby.SetFromGenome(0.0, new_genome, generation)
self.verify_baby(baby, mom, dad)
# else
baby.mut_struct_baby = mut_struct_baby
baby.mate_baby = mate_baby
curspecies_i = 0
found = False
while (curspecies_i < len(pop.species)) and (not found):
comporg = pop.species[curspecies_i].first()
if comporg == None:
curspecies_i += 1
elif baby.gnome.compatibility(comporg.gnome) < neat.compat_threshold:
pop.species[curspecies_i].add_Organism(baby)
baby.species = pop.species[curspecies_i]
found = True
else:
curspecies_i += 1
#
if not found:
pop.last_species += 1
newspecies = Species()
newspecies.SetFromIdAndNovel(pop.last_species, True)
pop.species.append(newspecies)
newspecies.add_Organism(baby)
baby.species = newspecies
# for count in self.expected_offspring
return True
