:py:mod:`desdeo_emo.EAs.BaseEA` =============================== .. py:module:: desdeo_emo.EAs.BaseEA Module Contents --------------- Classes ~~~~~~~ .. autoapisummary:: desdeo_emo.EAs.BaseEA.BaseEA desdeo_emo.EAs.BaseEA.BaseDecompositionEA .. py:exception:: eaError Bases: :py:obj:`Exception` Raised when an error related to EA occurs .. py:class:: BaseEA(a_priori: bool = False, interact: bool = False, selection_operator: Type[desdeo_emo.selection.SelectionBase.SelectionBase] = None, n_iterations: int = 10, n_gen_per_iter: int = 100, total_function_evaluations: int = 0, use_surrogates: bool = False, keep_archive: bool = False, save_non_dominated: bool = False) This class provides the basic structure for Evolutionary algorithms. .. py:method:: allowable_interaction_types() -> dict :property: .. py:method:: start() Mimics the structure of the mcdm methods. Returns the request objects from self.retuests(). .. py:method:: end() To be run at the end of the evolution process. .. py:method:: _next_gen() Run one generation of an EA. Change nothing about the parameters. .. py:method:: iterate(preference=None) -> Tuple Run one iteration of EA. One iteration consists of a constant or variable number of generations. This method leaves EA.params unchanged, except the current iteration count and gen count. .. py:method:: pre_iteration() Run this code before every iteration. .. py:method:: post_iteration() Run this code after every iteration. .. py:method:: continue_iteration() Checks whether the current iteration should be continued or not. .. py:method:: continue_evolution() -> bool Checks whether the current iteration should be continued or not. .. py:method:: check_FE_count() -> bool Checks whether termination criteria via function evaluation count has been met or not. :returns: True is function evaluation count limit NOT met. :rtype: bool .. py:method:: set_interaction_type(interaction_type: Union[None, str]) -> Union[None, str] .. py:method:: manage_preferences(preference=None) Forward the preference to the correct preference handling method. :param preference: _description_. Defaults to None. :type preference: _type_, optional :raises eaError: Preference handling not implemented. .. py:method:: requests() -> Tuple .. py:method:: non_dominated_archive(individuals: numpy.ndarray, results: desdeo_problem.EvaluationResults) -> None .. py:class:: BaseDecompositionEA(problem: desdeo_problem.MOProblem = None, initial_population: desdeo_emo.population.Population.Population = None, population_size: int = None, population_params: Dict = None, lattice_resolution: int = None, interact: bool = False, n_iterations: int = 10, n_gen_per_iter: int = 100, use_surrogates: bool = False, total_function_evaluations: int = 0, keep_archive: bool = False, save_non_dominated: bool = False) Bases: :py:obj:`BaseEA` The Base class for decomposition based EAs. This class contains most of the code to set up the parameters and operators. It also contains the logic of a simple decomposition EA. :param problem: The problem class object specifying the details of the problem. :type problem: MOProblem :param selection_operator: The selection operator to be used by the EA, by default None. :type selection_operator: Type[SelectionBase], optional :param population_size: The desired population size, by default None, which sets up a default value of population size depending upon the dimensionaly of the problem. :type population_size: int, optional :param population_params: The parameters for the population class, by default None. See desdeo_emo.population.Population for more details. :type population_params: Dict, optional :param initial_population: 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. :type initial_population: Population, optional :param lattice_resolution: The number of divisions along individual axes in the objective space to be used while creating the reference vector lattice by the simplex lattice design. By default None :type lattice_resolution: int, optional :param a_priori: A bool variable defining whether a priori preference is to be used or not. By default False :type a_priori: bool, optional :param interact: A bool variable defining whether interactive preference is to be used or not. By default False :type interact: bool, optional :param n_iterations: 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. :type n_iterations: int, optional :param n_gen_per_iter: 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. :type n_gen_per_iter: int, optional :param total_function_evaluations: 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. :type total_function_evaluations: int, optional .. py:method:: _next_gen() Run one generation of decomposition based EA. Intended to be used by next_iteration. .. py:method:: pre_iteration() Run this code before every iteration. .. py:method:: _select() -> list Describe a selection mechanism. Return indices of selected individuals. :returns: List of indices of individuals to be selected. :rtype: list .. py:method:: request_plot() -> desdeo_tools.interaction.SimplePlotRequest .. py:method:: request_preferences() -> Type[desdeo_tools.interaction.request.BaseRequest] .. py:method:: requests() -> Tuple .. py:method:: end() Conducts non-dominated sorting at the end of the evolution process :returns: 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. :rtype: tuple