culebra.fitness_func.MultiObjectiveFitnessFunction class

class MultiObjectiveFitnessFunction(*objectives: tuple[SingleObjectiveFitnessFunction, ...])

Bases: FitnessFunction

Construct a multi-objective fitness function.

Parameters:

objectives (tuple[SingleObjectiveFitnessFunction]) – Objectives for this fitness function

Class methods

classmethod MultiObjectiveFitnessFunction.load(filename: str) → Base

Load a serialized object from a file.

Parameters:

filename (str) – The file name.

Returns:

The loaded object

Raises:

• TypeError – If filename is not a valid file name

• ValueError – If the filename extension is not SERIALIZED_FILE_EXTENSION

Properties

property MultiObjectiveFitnessFunction.fitness_cls: type[Fitness]

Fitness class.

Return type:

type[Fitness]

property MultiObjectiveFitnessFunction.num_obj: int

Number of objectives.

Return type:

int

property MultiObjectiveFitnessFunction.obj_names: tuple[str, ...]

Objective names.

Return type:

tuple[str]

property MultiObjectiveFitnessFunction.obj_thresholds: tuple[float]

Objective similarity thresholds.

Return type:

tuple[float]

Setter:

Set new thresholds.

Parameters:

values (float | Sequence[float]) – The new values. If only a single value is provided, the same threshold will be used for all the objectives. Different thresholds can be provided in a Sequence. If set to None, all the thresholds are set to _default_similarity_threshold

Raises:

• TypeError – If neither a real number nor a Sequence of real numbers is provided

• ValueError – If any value is negative

• ValueError – If the length of the thresholds sequence does not match the number of objectives

property MultiObjectiveFitnessFunction.obj_weights: tuple[int, ...]

Objective weights.

Return type:

tuple[int]

property MultiObjectiveFitnessFunction.objectives: tuple[SingleObjectiveFitnessFunction]

Objectives to be optimized.

Return type:

tuple[SingleObjectiveFitnessFunction]

Private properties

property MultiObjectiveFitnessFunction._default_similarity_threshold: float

Default similarity threshold for fitnesses.

Returns:

DEFAULT_SIMILARITY_THRESHOLD

Return type:

float

Methods

MultiObjectiveFitnessFunction.dump(filename: str) → None

Serialize this object and save it to a file.

Parameters:

filename (str) – The file name.

Raises:

• TypeError – If filename is not a valid file name

• ValueError – If the filename extension is not SERIALIZED_FILE_EXTENSION

MultiObjectiveFitnessFunction.evaluate(sol: Solution, index: int | None = None, cooperators: Sequence[Solution | None] | None = None) → tuple[float, ...]

Evaluate a solution.

Neither the solution nor its fitness should be modified.

Parameters cooperators and index are used only for cooperative evaluations

Parameters:

• sol (Solution) – Solution to be evaluated.

• index (int) – Index where sol should be inserted in the cooperators sequence to form a complete solution for the problem, optional

• cooperators (Sequence[Solution]) – Cooperators of each species being optimized, optional

Returns:

The fitness for sol

Return type:

Fitness

Private methods

MultiObjectiveFitnessFunction._get_repr_properties() → dict[str, object]

Return the subset of properties used for __repr__.

Filters and evaluates all class-level @property attributes, returning only those intended for representation purposes. Private properties (names starting with _) are excluded.

Returns:

Mapping of property names to their corresponding values.

Return type:

dict[str, object]