from typing import Dict, Callable
import numpy as np
from desdeo_emo.population.Population import Population
from desdeo_emo.EAs.BaseIndicatorEA import BaseIndicatorEA
from desdeo_tools.utilities.quality_indicator import epsilon_indicator
from desdeo_emo.selection.TournamentSelection import TournamentSelection
from desdeo_problem import MOProblem
[docs]class IBEA(BaseIndicatorEA):
"""Python Implementation of IBEA.
Most of the relevant code is contained in the super class. This class just assigns
the EnviromentalSelection operator to BaseIndicatorEA.
Parameters
----------
problem: MOProblem
The problem class object specifying the details of the problem.
population_size : int, optional
The desired population size, by default None, which sets up a default value
of population size depending upon the dimensionaly of the problem.
population_params : Dict, optional
The parameters for the population class, by default None. See
desdeo_emo.population.Population for more details.
initial_population : Population, optional
An initial population class, by default None. Use this if you want to set up
a specific starting population, such as when the output of one EA is to be
used as the input of another.
a_priori : bool, optional
A bool variable defining whether a priori preference is to be used or not.
By default False
interact : bool, optional
A bool variable defining whether interactive preference is to be used or
not. By default False
n_iterations : int, optional
The total number of iterations to be run, by default 10. This is not a hard
limit and is only used for an internal counter.
n_gen_per_iter : int, optional
The total number of generations in an iteration to be run, by default 100.
This is not a hard limit and is only used for an internal counter.
total_function_evaluations : int, optional
Set an upper limit to the total number of function evaluations. When set to
zero, this argument is ignored and other termination criteria are used.
use_surrogates: bool, optional
A bool variable defining whether surrogate problems are to be used or
not. By default False
kappa : float, optional
Fitness scaling value for indicators. By default 0.05.
indicator : Callable, optional
Quality indicator to use in indicatorEAs. By default in IBEA this is additive epsilon indicator.
"""
def __init__(self,
problem: MOProblem,
population_size: int, # size required
initial_population: Population = None,
a_priori: bool = False,
interact: bool = False,
population_params: Dict = None,
n_iterations: int = 10,
n_gen_per_iter: int = 100,
total_function_evaluations: int = 0,
use_surrogates: bool = False,
kappa: float = 0.05, # fitness scaling ratio
indicator: Callable = epsilon_indicator, # default indicator is epsilon_indicator
):
super().__init__(
problem=problem,
population_size=population_size,
population_params=population_params,
a_priori=a_priori,
interact=interact,
n_iterations=n_iterations,
initial_population=initial_population,
n_gen_per_iter=n_gen_per_iter,
total_function_evaluations=total_function_evaluations,
use_surrogates=use_surrogates,
)
self.indicator = indicator
self.kappa = kappa
selection_operator = TournamentSelection(self.population, 2)
self.selection_operator = selection_operator
[docs] def _fitness_assignment(self, fitnesses):
"""
Performs the fitness assignment of the individuals.
"""
for i in range(self.population.individuals.shape[0]):
for j in range(self.population.individuals.shape[0]):
if j != i:
fitnesses[i] += -np.exp(-self.indicator(self.population.fitness[i],
self.population.fitness[j]) / self.kappa)
return fitnesses
[docs] def _environmental_selection(self, fitnesses, worst_index):
"""
Selects the worst member of population, then updates the population members fitness values compared to the worst individual.
Worst individual is removed from the population.
"""
# updates the fitness values
for i in range(self.population.individuals.shape[0]):
if worst_index != i:
fitnesses[i] += np.exp(-self.indicator(self.population.fitness[i], self.population.fitness[worst_index]) / self.kappa)
return fitnesses
