def mutate_individual(self, candidate):
if random.random() < self.prob_order_mutates:
candidate.order *= -1
if random.random() < self.prob_orientation_mutates:
candidate.orientation = candidate.orientation.negate()
# if random.random() < self.prob_t_mutates:
# candidate.t += random.uniform(-self.t_mutation_magnitude, self.t_mutation_magnitude)
if random.random() < self.prob_padding_mutates:
candidate.padding = not candidate.padding
candidate.t = min(max(candidate.t, 0.3), 0.7)
for child in candidate.children:
self.mutate_individual(child)
def mutate_individual(self, weights):
mutant = dict(weights)
for key in weights.keys():
if random.random() <= self.prob_point_mutation:
mutant[key] += random.uniform(-0.3, 0.3)
mutant[key] = max(0.05, mutant[key])
self.normalize_groom_weights(mutant)
mutant["scoreCurveExponent"] = max(1, mutant["scoreCurveExponent"])
return mutant
def mutate_individual(self, candidate):
for i in range(len(candidate.vector)):
if random.random() < self.prob_point_mutation:
candidate.vector[i] = 1 - candidate.vector[i]
def crossover_weights(self, a, b):
child = dict(a)
for key in a.keys():
if random.random() < self.prob_inherit_from_b:
child[key] = b[key]
return child