from pygenalgo.genome.chromosome import Chromosome
from pygenalgo.operators.genetic_operator import increase_counter
from pygenalgo.operators.selection.select_operator import SelectionOperator
[docs]
class RandomSelector(SelectionOperator):
"""
Description:
Random Selection involves choosing individuals from a population without any regard
to their fitness levels. Each member has an equal chance of being selected, which
promotes genetic diversity by allowing exploration of various solutions. While this
method is simple and easy to implement, it can lead to inefficient results, especially
in populations with varying fitness values. Random selection may overlook high-quality
solutions, making it less effective in later stages of evolution. However, it can serve
as a useful mechanism in the initial stages or when diversity is paramount for avoiding
premature convergence.
"""
def __init__(self, select_probability: float = 1.0) -> None:
"""
Construct a 'RandomSelector' 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]
@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)
# Select the new individuals indexes.
index = self.rng.choice(pop_size, size=pop_size, replace=True, shuffle=False)
# Return the new parents (individuals).
return [population[i] for i in index]
# _end_def_
# _end_class_