from operator import attrgetter
from functools import lru_cache
from pygenalgo.genome.chromosome import Chromosome
from pygenalgo.operators.genetic_operator import increase_counter
from pygenalgo.operators.selection.select_operator import SelectionOperator
[docs]
class LinearRankSelector(SelectionOperator):
"""
Description:
The LinearRank Selector is a selection mechanism in genetic algorithms that ranks
individuals based on their fitness values. It assigns selection probabilities to
candidates in a linear fashion, ensuring that higher-ranking individuals are more
likely to be chosen for reproduction. The operator helps maintain diversity in the
population by avoiding premature convergence, allowing lower-ranked individuals a
chance to contribute. This method is particularly effective in scenarios with varying
fitness levels, as it balances selection pressure while fostering exploration of the
solution space. Overall, it enhances the optimization process in evolutionary computation
models.
"""
def __init__(self, select_probability: float = 1.0) -> None:
"""
Construct a 'LinearRankSelector' object with a given probability value.
:param select_probability: (float) in [0, 1].
"""
# Call the super constructor with the provided probability value.
super().__init__(select_probability)
# _end_def_
[docs]
@staticmethod
@lru_cache(maxsize=32)
def probabilities(p_size: int) -> list[float]:
"""
Calculate the rank probability distribution over the population size.
The function is lru_cached so that repeated calls with the same input
should not recompute the same array, since the population size of the
chromosomes is not expected to change dynamically.
NOTE: Probabilities are returned in ascending order.
:param p_size: (int) population size.
:return: (list) rank probability distribution in ascending order.
"""
# Sanity check.
if p_size <= 0:
raise ValueError(f"Population size {p_size} must be > 0.")
# _end_if_
# Calculate the sum of '1 + 2 + 3 + ... + N'.
# We know that this is equal to: N * (N+1)/2.
sum_ranked_values = float(0.5 * p_size * (p_size + 1))
# Return the probability values (ascending order).
return [n / sum_ranked_values for n in range(1, p_size + 1)]
# _end_def_
[docs]
@increase_counter
def select(self, population: list[Chromosome]) -> list[Chromosome]:
"""
Select the individuals, from the input population, that will be
passed on to the next genetic operations of crossover and mutation
to form the new population of solutions.
:param population: a list of chromosomes to select the parents from.
:return: the selected parents population (as list of chromosomes).
"""
# Get the population size.
pop_size = len(population)
# Calculate the selection probabilities of each member
# in the population, using their ranking position.
selection_probs = LinearRankSelector.probabilities(pop_size)
# Sort the population in ascending order using their fitness value.
sorted_population = sorted(population, key=attrgetter("fitness"))
# Select the new individuals (indexes).
index = self.rng.choice(pop_size, size=pop_size, p=selection_probs,
replace=True, shuffle=False)
# Return the new parents (individuals).
return [sorted_population[i] for i in index]
# _end_def_
# _end_class_