desdeo_emo.EAs.BaseEA

Module Contents

Classes

BaseEA	This class provides the basic structure for Evolutionary algorithms.
BaseDecompositionEA	The Base class for decomposition based EAs.

exception desdeo_emo.EAs.BaseEA.eaError

Bases: Exception

Raised when an error related to EA occurs

class desdeo_emo.EAs.BaseEA.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.

start()

Mimics the structure of the mcdm methods. Returns the request objects from self.retuests().

end()

To be run at the end of the evolution process.

_next_gen()

Run one generation of an EA. Change nothing about the parameters.

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.

pre_iteration()

Run this code before every iteration.

post_iteration()

Run this code after every iteration.

continue_iteration()

Checks whether the current iteration should be continued or not.

continue_evolution() → bool

Checks whether the current iteration should be continued or not.

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.

Return type:

bool

set_interaction_type(interaction_type: None | str) → None | str

manage_preferences(preference=None)

Forward the preference to the correct preference handling method.

Parameters:

preference (_type_, optional) – _description_. Defaults to None.

Raises:

eaError – Preference handling not implemented.

requests() → Tuple

non_dominated_archive(individuals: numpy.ndarray, results: desdeo_problem.EvaluationResults) → None

class desdeo_emo.EAs.BaseEA.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: 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.

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.

• lattice_resolution (int, optional) – 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

• 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.

_next_gen()

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

pre_iteration()

Run this code before every iteration.

_select() → list

Describe a selection mechanism. Return indices of selected individuals.

Returns:

List of indices of individuals to be selected.

Return type:

list

request_plot() → desdeo_tools.interaction.SimplePlotRequest

request_preferences() → Type[desdeo_tools.interaction.request.BaseRequest]

requests() → Tuple

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.

Return type:

tuple