desdeo_emo.EAs.BaseIndicatorEA

Module Contents

Classes

BaseIndicatorEA

The Base class for indicator based EAs.
class desdeo_emo.EAs.BaseIndicatorEA.BaseIndicatorEA(problem: desdeo_problem.MOProblem, population_size: int, selection_operator: Type[desdeo_emo.selection.SelectionBase.SelectionBase] = None, population_params: Dict = None, initial_population: desdeo_emo.population.Population.Population = None, 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, fitnesses=None)[source]

Bases: desdeo_emo.EAs.BaseEA

The Base class for indicator based EAs.

This class contains most of the code to set up the parameters and operators. It also contains the logic of a indicator EA.

Parameters:

  • problem (MOProblem) – The problem class object specifying the details of the problem.

  • selection_operator (Type[SelectionBase], optional) – The selection operator to be used by the EA, by default None.

  • 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

end()[source]

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.

Return type:

tuple

_next_gen()[source]

Run one generation of indicator based EA. Intended to be used by next_iteration.

_select() → list[source]

Performs the selection, returns indices of selected individuals.

Returns:

List of indices of individuals to be selected.

Return type:

list

manage_preferences(preference=None)[source]

Run the interruption phase of EA.

Conducts the interaction with the user.

request_preferences() → desdeo_tools.interaction.ReferencePointPreference[source]
request_plot() → desdeo_tools.interaction.SimplePlotRequest[source]
requests() → Tuple[source]