desdeo_emo.EAs.PBEA
Module Contents
Classes
Python Implementation of PBEA. |
- class desdeo_emo.EAs.PBEA.PBEA(problem: desdeo_problem.MOProblem, population_size: int, initial_population: desdeo_emo.population.Population.Population, a_priori: bool = False, interact: bool = False, n_iterations: int = 10, n_gen_per_iter: int = 100, total_function_evaluations: int = 0, use_surrogates: bool = False, kappa: float = 0.05, indicator: Callable = preference_indicator, population_params: Dict = None, reference_point=None, delta: float = 0.1)[source]
Bases:
desdeo_emo.EAs.BaseIndicatorEA.BaseIndicatorEA
Python Implementation of PBEA.
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. For PBEA this is preference based quality indicator.
reference_point (np.ndarray) – The reference point that guides the PBEAs search.
delta (float, optional) – Spesifity for the preference based quality indicator. By default 0.01.