from operator import attrgetter
from pygenalgo.utils.auxiliary import SubPopulation
from pygenalgo.operators.migration.migration_operator import MigrationOperator
[docs]
class RandomMigration(MigrationOperator):
"""
Description:
Random Migration implements a "very basic" migration policy in which
each island migrates its best chromosome to a randomly selected population.
"""
def __init__(self, migration_probability: float = 0.95) -> None:
"""
Construct a 'RandomMigration' object with a given probability value.
:param migration_probability: (float) in [0, 1].
"""
# Call the super constructor with the provided probability value.
super().__init__(migration_probability)
# _end_def_
[docs]
def migrate(self, islands: list[SubPopulation]) -> None:
"""
Perform the migration operation on the list of SubPopulations.
:param islands: list[SubPopulation].
:return: None.
"""
# Perform the migration only if we have more than one
# active populations.
if len(islands) > 1:
# First find the best individual chromosome FROM EACH island.
best_chromosomes = [max(island_i.population, key=attrgetter("fitness"))
for island_i in islands]
# Shuffle the order of the best chromosomes list.
self.rng.shuffle(best_chromosomes)
# Go through all the islands.
for island_i, best_j in zip(islands, best_chromosomes):
# Perform the migration with a predefined probability.
if self.is_operator_applicable():
# Select randomly one individual chromosome.
idx = self.rng.integers(0, len(island_i.population), dtype=int)
# Replace the randomly selected chromosome with
# the pre-selected best one from the list above.
island_i.population[idx] = best_j.clone()
# _end_if_
# _end_for_
# Increase the migration counter.
self.inc_counter()
# _end_def_
# _end_class_