from desdeo_emo.population.Population import Population
from desdeo_emo.EAs.BaseEA import BaseEA, eaError
from desdeo_emo.selection.TournamentSelection import TournamentSelection
import numpy as np
[docs]class TournamentEA(BaseEA):
def __init__(
self,
problem,
initial_population: Population,
n_gen_per_iter: int = 10,
n_iterations: int = 10,
tournament_size: int = 5,
population_size: int = 500,
):
"""Run generations of evolutionary algorithm using tournament selection.
Parameters
----------
population : "Population"
This variable is updated as evolution takes place
ea_parameters : dict
Takes the EA parameters.
Returns
-------
Population:
Returns the Population after evolution.
"""
super().__init__(n_gen_per_iter=n_gen_per_iter, n_iterations=n_iterations)
if initial_population is None:
msg = "Provide initial population"
raise eaError(msg)
self.population = initial_population
self.target_pop_size = population_size
self.tournament_size = tournament_size
selection_operator = TournamentSelection(self.population, self.tournament_size)
self.selection_operator = selection_operator
[docs] def end(self):
"""Conducts non-dominated sorting at the end of the evolution process
Returns:
tuple: The first element is a 2-D array of the decision vectors of the non-dominated solutions.
The second element is a 2-D array of the corresponding objective values.
"""
non_dom = self.population.non_dominated_objectives()
return (
self.population.individuals[non_dom, :],
self.population.objectives[non_dom, :],
)
[docs] def _next_gen(self):
"""Run one generation of decomposition based EA.
This method leaves method.params unchanged. Intended to be used by
next_iteration.
Parameters
----------
population: "Population"
Population object
"""
selected = self.select()
offspring = self.population.mate(mating_individuals=selected)
self.population.delete(np.arange(len(self.population.individuals)))
self.population.add(offspring)
self._current_gen_count += 1
self._gen_count_in_curr_iteration += 1
self._function_evaluation_count += offspring.shape[0]
[docs] def select(self) -> list:
return self.selection_operator.do(self.population)
