porcupy.utils.population
========================

.. py:module:: porcupy.utils.population


Classes
-------

.. autoapisummary::

   porcupy.utils.population.PopulationCycle
   porcupy.utils.population.SelectionStrategies


Module Contents
---------------

.. py:class:: PopulationCycle(initial_pop_size: int, min_pop_size: int, max_iter: int, cycles: int = 2, reduction_strategy: str = 'linear')

   Class for managing cyclic population reduction in the Crested Porcupine Optimizer.

   This class implements the cyclic population reduction strategy, which is a key
   feature of the CPO algorithm. The population size varies cyclically throughout
   the optimization process to balance exploration and exploitation.

   :param initial_pop_size: Initial population size.
   :type initial_pop_size: int
   :param min_pop_size: Minimum population size during reduction.
   :type min_pop_size: int
   :param max_iter: Maximum number of iterations.
   :type max_iter: int
   :param cycles: Number of cycles for population reduction.
   :type cycles: int
   :param reduction_strategy: Strategy for population reduction ('linear', 'cosine', or 'exponential').
   :type reduction_strategy: str

   .. attribute:: initial_pop_size

      Initial population size.

      :type: int

   .. attribute:: min_pop_size

      Minimum population size during reduction.

      :type: int

   .. attribute:: max_iter

      Maximum number of iterations.

      :type: int

   .. attribute:: cycles

      Number of cycles for population reduction.

      :type: int

   .. attribute:: reduction_strategy

      Strategy for population reduction.

      :type: str


   .. py:attribute:: initial_pop_size


   .. py:attribute:: min_pop_size


   .. py:attribute:: max_iter


   .. py:attribute:: cycles
      :value: 2



   .. py:attribute:: reduction_strategy
      :value: 'linear'



   .. py:method:: calculate_pop_size(iteration: int) -> int

      Calculate the population size for the current iteration

      :param iteration: Current iteration number
      :type iteration: int

      :returns: Population size for the current iteration
      :rtype: int



.. py:class:: SelectionStrategies

   Class implementing various selection strategies for population reduction.

   This class provides methods for selecting which individuals to keep when
   reducing the population size.


   .. py:method:: best_selection(positions: numpy.ndarray, fitness: numpy.ndarray, new_size: int) -> Tuple[numpy.ndarray, numpy.ndarray]
      :staticmethod:


      Select the best individuals based on fitness

      :param positions: Current positions of all individuals
      :type positions: numpy.ndarray
      :param fitness: Current fitness values of all individuals
      :type fitness: numpy.ndarray
      :param new_size: New population size
      :type new_size: int

      :returns: A tuple containing (selected_positions, selected_fitness)
      :rtype: tuple



   .. py:method:: tournament_selection(positions: numpy.ndarray, fitness: numpy.ndarray, new_size: int, tournament_size: int = 3) -> Tuple[numpy.ndarray, numpy.ndarray]
      :staticmethod:


      Select individuals using tournament selection

      :param positions: Current positions of all individuals
      :type positions: numpy.ndarray
      :param fitness: Current fitness values of all individuals
      :type fitness: numpy.ndarray
      :param new_size: New population size
      :type new_size: int
      :param tournament_size: Size of each tournament
      :type tournament_size: int

      :returns: A tuple containing (selected_positions, selected_fitness)
      :rtype: tuple



   .. py:method:: roulette_wheel_selection(positions: numpy.ndarray, fitness: numpy.ndarray, new_size: int) -> Tuple[numpy.ndarray, numpy.ndarray]
      :staticmethod:


      Select individuals using roulette wheel selection

      :param positions: Current positions of all individuals
      :type positions: numpy.ndarray
      :param fitness: Current fitness values of all individuals
      :type fitness: numpy.ndarray
      :param new_size: New population size
      :type new_size: int

      :returns: A tuple containing (selected_positions, selected_fitness)
      :rtype: tuple



   .. py:method:: diversity_selection(positions: numpy.ndarray, fitness: numpy.ndarray, new_size: int, elite_fraction: float = 0.2) -> Tuple[numpy.ndarray, numpy.ndarray]
      :staticmethod:


      Select individuals based on fitness and diversity

      :param positions: Current positions of all individuals
      :type positions: numpy.ndarray
      :param fitness: Current fitness values of all individuals
      :type fitness: numpy.ndarray
      :param new_size: New population size
      :type new_size: int
      :param elite_fraction: Fraction of individuals to select based on fitness
      :type elite_fraction: float

      :returns: A tuple containing (selected_positions, selected_fitness)
      :rtype: tuple