truncation_selector.py

from operator import attrgetter
from pygenalgo.genome.chromosome import Chromosome
from pygenalgo.operators.genetic_operator import increase_counter
from pygenalgo.operators.selection.select_operator import SelectionOperator

class TruncationSelector(SelectionOperator):
    """
    Description:

        Truncation Selector, creates a new population using a pre-defined proportion of the old population.
        When this method is called, it sorts the individuals of the OLD population using their fitness and
        then using a predefined value (e.g. p=0.3 or 30%) selects repeatedly new individuals from the top
        0.3 percent of the old population, until we reach the required size of the NEW population.
    """

    def __init__(self, select_probability: float = 1.0, p: float = 0.3):
        """
        Construct a 'TruncationSelector' object with a given probability value.

        :param select_probability: (float) in [0, 1].

        :param p: proportion of the population that will reproduce (float).
        """

        # Call the super constructor with the provided probability value.
        super().__init__(select_probability)

        # The proportion value should be in [0.1, 0.9].
        self._items = max(min(float(p), 0.9), 0.1)
    # _end_def_

    @increase_counter
    def select(self, population: 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)

        # Sort the population in descending order using their fitness value.
        sorted_population = sorted(population, key=attrgetter("fitness"), reverse=True)

        # Select tne new parents using only the higher percentage '%' of
        # the old population (indexes).
        index = self.rng.choice(int(pop_size*self._items), size=pop_size,
                                replace=True, shuffle=True)

        # Return the new parents (individuals).
        return [sorted_population[i] for i in index]
    # _end_def_

# _end_class_