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 GaussianMutator(MutationOperator):
"""
Description:
Gaussian mutator, mutates the chromosome by selecting randomly a position
and perturbing it with a Gaussian random value to the current gene value.
"""
def __init__(self, mutate_probability: float = 0.1, sigma: float = 1.0,
lower_val: Optional[float] = None, upper_val: Optional[float] = None) -> None:
"""
Construct a 'GaussianMutator' object with a given probability value.
:param mutate_probability: (float).
:param sigma: (float) standard deviation of the Gaussian N(0, sigma).
: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 sigma parameter is float.
sigma = float(sigma)
# Ensure standard deviation is positive.
if sigma <= 0.0:
raise ValueError(f"{self.__class__.__name__}: "
f"Standard deviation must be positive.")
# _end_if_
# 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 = [sigma, lower_val, upper_val]
# _end_def_
[docs]
def mutate(self, individual: Chromosome) -> None:
"""
Perform the mutation operation by randomly adding the
Gaussian value to a randomly selected gene position.
: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 values from the placeholder variable.
sigma, xl, xu = self._items
# 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
# Calculate the new Gene value by sampling from N(value, sigma),
# ensuring it stays within limits.
individual[i].value = clamp(self.rng.normal(loc=old_value, scale=sigma),
xl, xu)
# Set the fitness to NaN.
individual.invalidate_fitness()
# Increase the mutator counter.
self.inc_counter()
# _end_def_
# _end_class_