culebra.fitness_function.tsp.abc.TSPFitnessFunction class

class TSPFitnessFunction

Bases: FitnessFunction

Construct the fitness function.

Class methods

classmethod TSPFitnessFunction.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 TSPFitnessFunction.fitness_cls: type[Fitness]

Fitness class.

Return type:

type[Fitness]

abstract property TSPFitnessFunction.heuristic: tuple[ndarray[float], ...]

Heuristic matrices.

This property must be overridden by subclasses.

Returns:

A sequence of heuristic matrices. One for each objective. Arcs from a node to itself have a heuristic value of 0. For the rest of arcs, the reciprocal of their nodes distance is used as heuristic

Return type:

tuple[ndarray[float]]

Raises:

NotImplementedError – If has not been overridden

abstract property TSPFitnessFunction.num_nodes: int

Number of nodes of the problem graph.

This property must be overridden by subclasses to return the problem graph’s number of nodes.

Return type:

int

Raises:

NotImplementedError – If has not been overridden

property TSPFitnessFunction.num_obj: int

Number of objectives.

Return type:

int

abstract property TSPFitnessFunction.obj_names: tuple[str, ...]

Objective names.

This property must be overridden by subclasses to return a correct value.

Return type:

tuple[str]

property TSPFitnessFunction.obj_thresholds: list[float]

Objective similarity thresholds.

Return type:

list[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

abstract property TSPFitnessFunction.obj_weights: tuple[int, ...]

Objective weights.

This property must be overridden by subclasses to return a correct value.

Return type:

tuple[int]

Raises:

NotImplementedError – If has not been overridden

Private properties

property TSPFitnessFunction._default_similarity_threshold: float

Default similarity threshold for fitnesses.

Returns:

DEFAULT_SIMILARITY_THRESHOLD

Return type:

float

Methods

TSPFitnessFunction.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

abstract TSPFitnessFunction.evaluate(sol: Solution, index: int | None = None, representatives: Sequence[Solution] | None = None) → Fitness

Evaluate a solution.

Parameters representatives and index are used only for cooperative evaluations

This method must be overridden by subclasses to return a correct value.

Parameters:

• sol (Solution) – Solution to be evaluated.

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

• representatives (Sequence[Solution]) – Representative solutions of each species being optimized, optional

Returns:

The fitness for sol

Return type:

Fitness

Raises:

NotImplementedError – If has not been overridden