from typing import Optional
from pygenalgo.utils.utilities import clamp
from pygenalgo.genome.chromosome import Chromosome
from pygenalgo.operators.mutation.mutate_operator import MutationOperator
[docs]
class PolynomialMutator(MutationOperator):
"""
Description:
Polynomial mutator (PM-eta), mutates the chromosome by adjusting the
values of genes according to a polynomial distribution. This results
in a more controlled and smoother alteration of values.
"""
def __init__(self, mutate_probability: float = 0.1, eta_pm: float = 20.0,
lower_val: Optional[float] = None, upper_val: Optional[float] = None) -> None:
"""
Construct a 'PolynomialMutator' object with a given probability value.
:param mutate_probability: (float).
:param eta_pm: (float) the distribution index for polynomial mutation.
Higher values mean smaller perturbations (more local search).
:param lower_val: (float) lower limit value for the gene mutation.
:param upper_val: (float) upper limit value for the gene mutation.
"""
# Call the super constructor with the provided
# probability value.
super().__init__(mutate_probability)
# Ensure eta_pm parameter is float.
eta_pm = float(eta_pm)
# Ensure that both lower and upper limits are provided.
if lower_val is None or upper_val is None:
raise ValueError(f"{self.__class__.__name__}: "
f"Lower or Upper limits are missing.")
# _end_if_
# Ensure lower_val parameter is float.
lower_val = float(lower_val)
# Ensure upper_val parameter is float.
upper_val = float(upper_val)
# Ensure the order is correct.
if upper_val <= lower_val:
raise ValueError(f"{self.__class__.__name__}: "
f"The limit values are incorrect.")
# _end_if_
# Assign variables to the _items placeholder.
self._items = [eta_pm, lower_val, upper_val]
# _end_def_
[docs]
def mutate(self, individual: Chromosome) -> None:
"""
Perform the mutation operation by adjusting the values
of genes according to a polynomial distribution.
:param individual: (Chromosome).
:return: None.
"""
# If the mutation probability is higher than
# a uniformly random value, make the changes.
if self.is_operator_applicable():
# Get the size of the chromosome.
n_genes: int = len(individual)
# Extract the variables from the placeholder _items.
eta_pm, xl, xu = self._items
# Generate a random number in [0, 1).
rand_u: float = self.rng.random()
# Calculate delta (the perturbation factor).
if rand_u <= 0.5:
delta = ((2.0 * rand_u) ** (1.0 / (eta_pm + 1.0))) - 1.0
else:
delta = 1.0 - ((2.0 * (1.0 - rand_u)) ** (1.0 / (eta_pm + 1.0)))
# _end_if_
# Select a random position in the genome.
i = self.rng.integers(n_genes, dtype=int)
# Get the old value of the Gene.
old_value = individual[i].value
# Update the genome of the offspring with the new value ensuring it
# stays within limits.
individual[i].value = clamp(old_value + delta * (xu - xl), xl, xu)
# Set the fitness to NaN.
individual.invalidate_fitness()
# Increase the mutator counter.
self.inc_counter()
# _end_def_
# _end_class_