:py:mod:`desdeo_emo.EAs.IOPIS` ============================== .. py:module:: desdeo_emo.EAs.IOPIS Module Contents --------------- Classes ~~~~~~~ .. autoapisummary:: desdeo_emo.EAs.IOPIS.BaseIOPISDecompositionEA desdeo_emo.EAs.IOPIS.IOPIS_RVEA desdeo_emo.EAs.IOPIS.IOPIS_NSGAIII .. py:class:: BaseIOPISDecompositionEA(problem: desdeo_problem.MOProblem, population_size: int = None, population_params: Dict = None, initial_population: desdeo_emo.population.Population.Population = None, lattice_resolution: int = None, n_iterations: int = 10, n_gen_per_iter: int = 100, total_function_evaluations: int = 0, use_surrogates: bool = False) Bases: :py:obj:`desdeo_emo.EAs.BaseEA.BaseDecompositionEA`, :py:obj:`desdeo_emo.EAs.BaseEA.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:: manage_preferences(preference=None) Run the interruption phase of EA. Use this phase to make changes to RVEA.params or other objects. Updates Reference Vectors (adaptation), conducts interaction with the user. .. py:method:: request_preferences() -> Union[None, desdeo_tools.interaction.ReferencePointPreference] .. 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:class:: IOPIS_RVEA(problem: desdeo_problem.MOProblem, population_size: int = None, population_params: Dict = None, initial_population: desdeo_emo.population.Population.Population = None, alpha: float = None, lattice_resolution: int = None, n_iterations: int = 10, n_gen_per_iter: int = 100, total_function_evaluations: int = 0, time_penalty_component: Union[str, float] = None, use_surrogates: bool = False) Bases: :py:obj:`BaseIOPISDecompositionEA`, :py:obj:`desdeo_emo.EAs.RVEA.RVEA` The python version reference vector guided evolutionary algorithm. Most of the relevant code is contained in the super class. This class just assigns the APD selection operator to BaseDecompositionEA. NOTE: The APD function had to be slightly modified to accomodate for the fact that this version of the algorithm is interactive, and does not have a set termination criteria. There is a time component in the APD penalty function formula of the type: (t/t_max)^alpha. As there is no set t_max, the formula has been changed. See below, the documentation for the argument: penalty_time_component See the details of RVEA in the following paper R. Cheng, Y. Jin, M. Olhofer and B. Sendhoff, A Reference Vector Guided Evolutionary Algorithm for Many-objective Optimization, IEEE Transactions on Evolutionary Computation, 2016 :param problem: The problem class object specifying the details of the problem. :type problem: MOProblem :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 alpha: The alpha parameter in the APD selection mechanism. Read paper for details. :type alpha: float, 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 :param penalty_time_component: The APD formula had to be slightly changed. If penalty_time_component is a float between [0, 1], (t/t_max) is replaced by that constant for the entire algorithm. If penalty_time_component is "original", the original intent of the paper is followed and (t/t_max) is calculated as (current generation count/total number of generations). If penalty_time_component is "function_count", (t/t_max) is calculated as (current function evaluation count/total number of function evaluations) If penalty_time_component is "interactive", (t/t_max) is calculated as (Current gen count within an iteration/Total gen count within an iteration). Hence, time penalty is always zero at the beginning of each iteration, and one at the end of each iteration. Note: If the penalty_time_component ever exceeds one, the value one is used as the penalty_time_component. If no value is provided, an appropriate default is selected. If `interact` is true, penalty_time_component is "interactive" by default. If `interact` is false, but `total_function_evaluations` is provided, penalty_time_component is "function_count" by default. If `interact` is false, but `total_function_evaluations` is not provided, penalty_time_component is "original" by default. :type penalty_time_component: Union[str, float], optional .. py:method:: _time_penalty_constant() Returns the constant time penalty value. .. py:method:: _time_penalty_original() Calculates the appropriate time penalty value, by the original formula. .. py:method:: _time_penalty_interactive() Calculates the appropriate time penalty value. .. py:method:: _time_penalty_function_count() Calculates the appropriate time penalty value. .. py:class:: IOPIS_NSGAIII(problem: desdeo_problem.MOProblem, population_size: int = None, population_params: Dict = None, initial_population: desdeo_emo.population.Population.Population = None, lattice_resolution: int = None, n_iterations: int = 10, n_gen_per_iter: int = 100, total_function_evaluations: int = 0, use_surrogates: bool = False) Bases: :py:obj:`BaseIOPISDecompositionEA` 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