linear_rank_selector.py

from pygenalgo.genome.chromosome import Chromosome
from pygenalgo.operators.selection.select_operator import SelectionOperator


class LinearRankSelector(SelectionOperator):
    """
    Description:

        Linear Rank Selector implements an object that performs selection using ranking.
        The individuals first are sorted according to their fitness values. The rank 'N'
        is assigned to the best individual and the rank 1 to the worst individual.

        After that the selection process is similar to the one of RouletteWheelSelector.
    """

    def __init__(self, select_probability: float = 1.0):
        """
        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_

    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).
        """

        # Sort the population in ascending order using their fitness value.
        sorted_population = sorted(population, key=lambda p: p.fitness)

        # Get the length of the population list.
        N = len(sorted_population)

        # Calculate sum of all the ranked fitness values: "1 + 2 + 3 + ... + N".
        sum_ranked_values = float(0.5 * N * (N + 1))

        # Calculate the "selection probabilities", of each member in the population.
        selection_probs = [n / sum_ranked_values for n in range(1, N + 1)]

        # Increase the selection counter.
        self.inc_counter()

        # Select 'N' new individuals (indexes).
        index = self.rng.choice(N, size=N, p=selection_probs, replace=True, shuffle=False)

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

# _end_class_