desdeo_emo.EAs.IBEA

Module Contents

Classes

IBEA

Python Implementation of IBEA.

class desdeo_emo.EAs.IBEA.IBEA(problem: desdeo_problem.MOProblem, population_size: int, initial_population: desdeo_emo.population.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, indicator: Callable = epsilon_indicator)[source]

Bases: desdeo_emo.EAs.BaseIndicatorEA.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.

_fitness_assignment(fitnesses)[source]

Performs the fitness assignment of the individuals.

_environmental_selection(fitnesses, worst_index)[source]

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.