`culebra.trainer.aco.abc.SingleObjPACO` class¶

Bases: MaxPheromonePACO, SingleObjACO

Create a new population-based ACO trainer.

Parameters:

solution_cls (type[Ant]) – The solution class
species (Species) – The species for all the ants
fitness_function (FitnessFunction) – The training fitness function
initial_pheromone (float | Sequence[float]) – Initial amount of pheromone for the paths of each pheromone matrix. Both a scalar value or a sequence of values are allowed. If a scalar value is provided, it will be used for all the num_pheromone_matrices pheromone matrices.
max_pheromone (float | Sequence[float]) – Maximum amount of pheromone for the paths of each pheromone matrix. Both a scalar value or a sequence of values are allowed. If a scalar value is provided, it will be used for all the num_pheromone_matrices pheromone matrices.
heuristic (ndarray[float] | Sequence[ndarray[float], ...]) – Heuristic matrices. Both a single matrix or a sequence of matrices are allowed. If a single matrix is provided, it will be replicated for all the num_heuristic_matrices heuristic matrices. If omitted, _default_heuristic will be used. Defaults to None
pheromone_influence (float | Sequence[float]) – Relative influence of each pheromone matrix (\({\alpha}\)). Both a scalar value or a sequence of values are allowed. If a scalar value is provided, it will be used for all the num_pheromone_matrices pheromone matrices. If omitted, _default_pheromone_influence will be used. Defaults to None
heuristic_influence (float | Sequence[float]) – Relative influence of each heuristic (\({\beta}\)). Both a scalar value or a sequence of values are allowed. If a scalar value is provided, it will be used for all the num_heuristic_matrices heuristic matrices. If omitted, _default_heuristic_influence will be used. Defaults to None
exploitation_prob (float) – Probability to make the best possible move (\({q_0}\)). If omitted, _default_exploitation_prob will be used. Defaults to None
max_num_iters (int) – Maximum number of iterations. If omitted, _default_max_num_iters will be used. Defaults to None
custom_termination_func (Callable) – Custom termination criterion. If omitted, _default_termination_func() is used. Defaults to None
col_size (int) – The colony size. If omitted, _default_col_size will be used. Defaults to None
pop_size (int) – The population size. If omitted, _default_pop_size will be used. Defaults to None
checkpoint_activation (bool) – Checkpoining activation. If omitted, _default_checkpoint_activation will be used. Defaults to None
checkpoint_freq (int) – The checkpoint frequency. If omitted, _default_checkpoint_freq will be used. Defaults to None
checkpoint_filename (str) – The checkpoint file path. If omitted, _default_checkpoint_filename will be used. Defaults to None
verbosity (bool) – The verbosity. If omitted, _default_verbosity will be used. Defaults to None
random_seed (int) – The seed. Defaults to None

Raises:

TypeError – If any argument is not of the appropriate type
ValueError – If any argument has an incorrect value

Class attributes¶

SingleObjPACO.objective_stats = {'Avg': <function mean>, 'Max': <function max>, 'Min': <function min>, 'Std': <function std>}¶: Statistics calculated for each objective.

SingleObjPACO.stats_names = ('Iter', 'NEvals')¶: Statistics calculated each iteration.

Class methods¶

classmethod SingleObjPACO.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 SingleObjPACO.checkpoint_activation: bool¶

Checkpointing activation.

Returns:: True if checkpointing is active, or False otherwise
Return type:: bool
Setter:: Modify the checkpointing activation
Parameters:: value (bool) – New value for the checkpoint activation. If set to None, _default_checkpoint_activation is chosen
Raises:: TypeError – If value is not a boolean value

property SingleObjPACO.checkpoint_filename: str¶

Checkpoint file path.

Return type:

str

Setter:

Modify the checkpoint file path

Parameters:

value (str) – New value for the checkpoint file path. If set to None, _default_checkpoint_filename is chosen

Raises:

TypeError – If value is not a valid file name
ValueError – If the value extension is not SERIALIZED_FILE_EXTENSION

property SingleObjPACO.checkpoint_freq: int¶

Checkpoint frequency.

Return type:

int

Setter:

Modify the checkpoint frequency

Parameters:

value (int) – New value for the checkpoint frequency. If set to None, _default_checkpoint_freq is chosen

Raises:

TypeError – If value is not an integer
ValueError – If value is not a positive number

property SingleObjPACO.choice_info: ndarray[float] | None¶

Choice information for all the graph’s arcs.

The choice information is generated from both the pheromone and the heuristic matrices, modified by other parameters (depending on the ACO approach) and is used to obtain the probalility of following the next feasible arc for the node.

Returns:: The choice information or None if the search process has not begun
Return type:: ndarray[float]

property SingleObjPACO.col: list[Ant] | None¶

Colony.

Returns:: The colony or None if it has not been generated yet
Return type:: list[Ant]

property SingleObjPACO.col_size: int¶

Colony size.

Return type:

int

Setter:

Set a new value for the colony size

Parameters:

size (int) – The new colony size. If set to None, _default_col_size is chosen

Raises:

TypeError – If size is not an int
ValueError – If size is not greater than zero

property SingleObjPACO.container: Trainer | None¶

Container of this trainer.

The trainer container is only used by distributed trainers. For the rest of trainers defaults to None.

Return type:: Trainer
Setter:: Set a new value for container of this trainer
Parameters:: value (Trainer) – New value for the container or None
Raises:: TypeError – If value is not a valid trainer

property SingleObjPACO.current_iter: int | None¶

Current iteration.

Returns:: The current iteration or None if the search has not been done yet
Return type:: int

property SingleObjPACO.custom_termination_func: Callable[[Trainer], bool] | None¶

Custom termination criterion.

Although the trainer will always stop when the max_num_iters are reached, a custom termination criterion can be set to detect convergente and stop the trainer earlier. This custom termination criterion must be a function which receives the trainer as its unique argument and returns a boolean value, True if the search should terminate or False otherwise.

If more than one arguments are needed to define the termination condition, functools.partial() can be used:

from functools import partial

def my_crit(trainer, max_iters):
    if trainer.current_iter < max_iters:
        return False
    return True

trainer.custom_termination_func = partial(my_crit, max_iters=10)

Setter:: Set a new custom termination criterion
Parameters:: func (Callable) – The new custom termination criterion. If set to None, the default termination criterion is used
Raises:: TypeError – If func is not callable

property SingleObjPACO.exploitation_prob: float¶

Exploitation probability (\({q_0}\)).

Return type:

float

Setter:

Set a new value for the exploitation probability

Parameters:

prob (float) – The new probability. If set to None, _default_exploitation_prob is chosen

Raises:

TypeError – If prob is not a real number
ValueError – If prob is not in [0, 1]

property SingleObjPACO.fitness_function: FitnessFunction¶

Training fitness function.

Return type:: FitnessFunction
Setter:: Set a new fitness function
Parameters:: func (FitnessFunction) – The new training fitness function
Raises:: TypeError – If func is not a valid fitness function

property SingleObjPACO.heuristic: tuple[ndarray[float], ...]¶

Heuristic matrices.

Return type:

tuple[ndarray[float]]

Setter:

Set new heuristic matrices

Parameters:

values (ndarray[float] | Sequence[ndarray[float], ...]) – The new heuristic matrices. Both a single matrix or a sequence of matrices are allowed. If a single matrix is provided, it will be replicated for all the heuristic matrices. If set to None, _default_heuristic is chosen

Raises:

TypeError – If values is neither an numpy.ndarray nor a Sequence of numpy.ndarray
ValueError – If values is a sequence of numpy.ndarray and its length is not num_heuristic_matrices
ValueError – If any array has not the correct shape
ValueError – If any element in any array object is not a float number
ValueError – If any element in any array object is negative

property SingleObjPACO.heuristic_influence: tuple[float, ...]¶

Relative influence of heuristic (\({\beta}\)).

Returns:

One value for each heuristic matrix

Return type:

tuple[float]

Setter:

Set new values for the relative influence of each heuristic matrix

Parameters:

values (float | Sequence[float]) – New value for the relative influence of each heuristic matrix. Both a scalar value or a sequence of values are allowed. If a scalar value is provided, it will be used for all the heuristic matrices. If set to None, _default_heuristic_influence is chosen

Raises:

TypeError – If values is neither a float nor a Sequence of float values
ValueError – If any element in values is negative
ValueError – If values is a sequence and it length is different from num_heuristic_matrices

abstract property SingleObjPACO.heuristic_shapes: tuple[tuple[int, int], ...]¶

Shape of the heuristic matrices.

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

Return type:: tuple[tuple[int]]
Raises:: NotImplementedError – If has not been overridden

property SingleObjPACO.index: int¶

Trainer index.

The trainer index is only used by distributed trainers. For the rest of trainers _default_index is used.

Return type:

int

Setter:

Set a new value for trainer index.

Parameters:

value (int) – New value for the trainer index. If set to None, _default_index is chosen

Raises:

TypeError – If value is not an integer
ValueError – If value is a negative number

property SingleObjPACO.initial_pheromone: tuple[float, ...]¶

Initial value for each pheromone matrix.

Returns:

One initial value for each pheromone matrix

Return type:

tuple[float]

Setter:

Set the initial value for each pheromone matrix

Parameters:

values (float | Sequence[float]) – New initial value for each pheromone matrix. Both a scalar value or a sequence of values are allowed. If a scalar value is provided, it will be used for all the pheromone matrices

Raises:

TypeError – If values is neither a float nor a Sequence of float values
ValueError – If any element in values is negative or zero
ValueError – If values is a sequence and it length is different from num_pheromone_matrices

property SingleObjPACO.logbook: Logbook | None¶

Trainer logbook.

Returns:: A logbook with the statistics of the search or None if the search has not been done yet
Return type:: Logbook

property SingleObjPACO.max_num_iters: int¶

Maximum number of iterations.

Return type:

int

Setter:

Set a new value for the maximum number of iterations

Parameters:

value (int) – The new maximum number of iterations. If set to None, the default maximum number of iterations, _default_max_num_iters, is chosen

Raises:

TypeError – If value is not an integer
ValueError – If value is not a positive number

property SingleObjPACO.max_pheromone: tuple[float, ...]¶

Maximum value for each pheromone matrix.

Return type:

tuple[float]

Setter:

Set the maximum value for each pheromone matrix

Parameters:

values (float | Sequence[float]) – New maximum value for each pheromone matrix. Both a scalar value or a sequence of values are allowed. If a scalar value is provided, it will be used for all the num_pheromone_matrices pheromone matrices.

Raises:

TypeError – If values is neither a float nor a Sequence of float values
ValueError – If any element in values is negative or zero
ValueError – If any element in values is lower than or equal to its corresponding initial pheromone value
ValueError – If values is a sequence and it length is different from num_pheromone_matrices

property SingleObjPACO.num_evals: int | None¶

Number of evaluations performed while training.

Returns:: The number of evaluations or None if the search has not been done yet
Return type:: int

property SingleObjPACO.num_heuristic_matrices: int¶

Number of heuristic matrices used by this trainer.

Return type:: int

property SingleObjPACO.num_pheromone_matrices: int¶

Number of pheromone matrices used by this trainer.

Return type:: int

property SingleObjPACO.pheromone: list[ndarray[float], ...] | None¶

Pheromone matrices.

Returns:: The pheromone matrices or None if the search process has not begun
Return type:: list[ndarray[float]]

property SingleObjPACO.pheromone_influence: tuple[float, ...]¶

Relative influence of pheromone (\({\alpha}\)).

Returns:

One value for each pheromone matrix

Return type:

tuple[float]

Getter:

Return the relative influence of each pheromone matrix.

Setter:

Set new values for the relative influence of each pheromone matrix

Parameters:

values (float | Sequence[float]) – New value for the relative influence of each pheromone matrix. Both a scalar value or a sequence of values are allowed. If a scalar value is provided, it will be used for all the pheromone matrices. If set to None, _default_pheromone_influence is chosen

Raises:

TypeError – If values is neither a float nor a Sequence of float values
ValueError – If any element in values is negative
ValueError – If values is a sequence and it length is different from num_pheromone_matrices

abstract property SingleObjPACO.pheromone_shapes: tuple[tuple[int, int], ...]¶

Shape of the pheromone matrices.

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

Return type:: tuple[tuple[int]]
Raises:: NotImplementedError – If has not been overridden

property SingleObjPACO.pop: list[Ant] | None¶

Population.

Returns:: The population or None if it has not been generated
Return type:: list[Ant]

property SingleObjPACO.pop_ingoing: list[Ant] | None¶

Ants inserted into the population in the last population update.

Returns:: A list of ants or :data`None` if the searh has not been initialized
Return type:: list[Ant]

property SingleObjPACO.pop_outgoing: list[Ant] | None¶

Ants removed from the population in the last population update.

Returns:: A list of ants or :data`None` if the searh has not been initialized
Return type:: list[Ant]

property SingleObjPACO.pop_size: int¶

Population size.

Return type:

int

Setter:

Set the population size

Parameters:

size (int) – The new population size. If set to None, _default_pop_size is chosen

Raises:

TypeError – If size is not an int
ValueError – If size is not greater than zero

property SingleObjPACO.random_seed: int¶

Random seed used by this trainer.

Return type:: int
Setter:: Set a new value for the random seed
Parameters:: value (int) – New value

property SingleObjPACO.representatives: list[list[Solution | None]] | None¶

Representatives of the other species.

Only used by cooperative trainers. If the trainer does not use representatives, None is returned.

Return type:: list[list[Solution]]

property SingleObjPACO.runtime: float | None¶

Training runtime.

Returns:: The training runtime or None if the search has not been done yet.
Return type:: float

property SingleObjPACO.solution_cls: type[Solution]¶

Solution class.

Return type:: type[Solution]
Setter:: Set a new solution class
Parameters:: cls (type[Solution]) – The new class
Raises:: TypeError – If cls is not valid solution class

property SingleObjPACO.species: Species¶

Species.

Return type:: Species
Setter:: Set a new species
Parameters:: value (Species) – The new species
Raises:: TypeError – If value is not a valid species

property SingleObjPACO.verbosity: bool¶

Verbosity of this trainer.

Return type:: bool
Setter:: Set a new value for the verbosity
Parameters:: value (bool) – The verbosity. If set to None, _default_verbosity is chosen
Raises:: TypeError – If value is not boolean

Private properties¶

property SingleObjPACO._default_checkpoint_activation: bool¶

Default checkpointing activation.

Returns:: DEFAULT_CHECKPOINT_ACTIVATION
Return type:: bool

property SingleObjPACO._default_checkpoint_filename: str¶

Default checkpointing file name.

Returns:: DEFAULT_CHECKPOINT_FILENAME
Return type:: str

property SingleObjPACO._default_checkpoint_freq: int¶

Default checkpointing frequency.

Returns:: DEFAULT_CHECKPOINT_FREQ
Return type:: int

abstract property SingleObjPACO._default_col_size: int¶

Default colony size.

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

Return type:: int
Raises:: NotImplementedError – If has not been overridden

property SingleObjPACO._default_exploitation_prob: float¶

Default exploitation probability (\({q_0}\)).

Returns:: attr:~culebra.trainer.aco.DEFAULT_EXPLOITATION_PROB
Return type:: float

abstract property SingleObjPACO._default_heuristic: tuple[ndarray[float], ...]¶

Default heuristic matrices.

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

Return type:: tuple[ndarray[float]]
Raises:: NotImplementedError – If has not been overridden

property SingleObjPACO._default_heuristic_influence: tuple[float, ...]¶

Default relative influence of heuristic (\({\beta}\)).

Returns:: The DEFAULT_HEURISTIC_INFLUENCE for each pheromone matrix
Return type:: tuple[float]

property SingleObjPACO._default_index: int¶

Default index.

Returns:: DEFAULT_INDEX
Return type:: int

property SingleObjPACO._default_max_num_iters: int¶

Default maximum number of iterations.

Returns:: DEFAULT_MAX_NUM_ITERS
Return type:: int

property SingleObjPACO._default_pheromone_influence: tuple[float, ...]¶

Default relative influence of pheromone (\({\alpha}\)).

Returns:: The DEFAULT_PHEROMONE_INFLUENCE for each pheromone matrix
Return type:: tuple[float]

property SingleObjPACO._default_pop_size: int¶

Default Population size.

Returns:: col_size
Return type:: int

property SingleObjPACO._default_verbosity: bool¶

Default verbosity.

Returns:: DEFAULT_VERBOSITY
Return type:: bool

Methods¶

SingleObjPACO.best_representatives() → list[list[Solution]] | None¶

Return a list of representatives from each species.

Only used for cooperative trainers.

Returns:: A list of representatives lists if the trainer is cooperative or None in other cases.
Return type:: list[list[Solution]]

SingleObjPACO.best_solutions() → tuple[HallOfFame]¶

Get the best solutions found for each species.

Returns:: One Hall of Fame for each species
Return type:: tuple[HallOfFame]

SingleObjPACO.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

SingleObjPACO.evaluate(sol: Solution, fitness_func: FitnessFunction | None = None, index: int | None = None, representatives: Sequence[Sequence[Solution | None]] | None = None) → None¶

Evaluate one solution.

Its fitness will be modified according with the fitness function results. Besides, if called during training, the number of evaluations will be also updated.

Parameters:

sol (Solution) – The solution
fitness_func (FitnessFunction) – The fitness function. If omitted, the default training fitness function (fitness_function) is used
index (int) – Index where sol should be inserted in the representatives sequence to form a complete solution for the problem. If omitted, index is used
representatives (Sequence[Sequence[Solution]]) – Sequence of representatives of other species or None (if no representatives are needed to evaluate sol). If omitted, the current value of representatives is used

SingleObjPACO.reset() → None¶

Reset the trainer.

Delete the state of the trainer (with _reset_state()) and also all the internal data structures needed to perform the search (with _reset_internals()).

This method should be invoqued each time a hyper parameter is modified.

SingleObjPACO.test(best_found: Sequence[HallOfFame], fitness_func: FitnessFunction | None = None, representatives: Sequence[Sequence[Solution]] | None = None) → None¶

Apply the test fitness function to the solutions found.

Update the solutions in best_found with their test fitness.

Parameters:

best_found (Sequence[HallOfFame]) – The best solutions found for each species. One HallOfFame for each species
fitness_func (FitnessFunction) – Fitness function used to evaluate the final solutions. If ommited, the default training fitness function (fitness_function) will be used
representatives (Sequence[Sequence[Solution]]) – Sequence of representatives of other species or None (if no representatives are needed). If omitted, the current value of representatives is used

Raises:

TypeError – If any parameter has a wrong type
ValueError – If any parameter has an invalid value.

SingleObjPACO.train(state_proxy: DictProxy | None = None) → None¶

Perform the training process.

Parameters:: state_proxy (DictProxy) – dictionary proxy to copy the output state of the trainer procedure. Only used if train is executed within a multiprocess.Process. Defaults to None

Private methods¶

SingleObjPACO._ant_choice_info(ant: Ant) → ndarray[float]¶

Return the choice info to obtain the next node the ant will visit.

All the previously visited nodes are discarded. Subclasses should override this method if the Species constraining the solutions of the problem supports node banning.

Parameters:: ant (Ant) – The ant
Return type:: ndarray[float]

abstract SingleObjPACO._calculate_choice_info() → None¶

Calculate the choice information.

This method should be overridden by subclasses.

Raises:: NotImplementedError – If has not been overridden

SingleObjPACO._decrease_pheromone() → None¶

Decrease the amount of pheromone.

Ants leaving the population remove pheromone.

SingleObjPACO._default_termination_func() → bool¶

Default termination criterion.

Returns:: True if max_num_iters iterations have been run
Return type:: bool

SingleObjPACO._deposit_pheromone(ants: Sequence[Ant], weight: float = 1.0) → None¶

Make some ants deposit weighted pheromone.

This method must be overridden by subclasses to take into account the correct number and shape of the pheromone matrices.

Parameters:

ants (Sequence[Ant]) – The ants
weight (float) – Weight for the pheromone. Defaults to DEFAULT_PHEROMONE_DEPOSIT_WEIGHT

Raises:

NotImplementedError – If has not been overridden

SingleObjPACO._do_iteration() → None¶: Implement an iteration of the search process.

SingleObjPACO._do_iteration_stats() → None¶: Perform the iteration stats.

SingleObjPACO._finish_iteration() → None¶

Finish an iteration.

Finish the iteration metrics (number of evaluations, execution time) after each iteration is run.

SingleObjPACO._finish_search() → None¶

Finish the search process.

This method is called after the search has finished. It can be overridden to perform any treatment of the solutions found.

SingleObjPACO._generate_ant() → Ant¶

Generate a new ant.

The ant makes its path and gets evaluated.

Returns:: The new ant
Return type:: Ant

SingleObjPACO._generate_col() → None¶: Fill the colony with evaluated ants.

SingleObjPACO._get_state() → dict[str, Any]¶

Return the state of this trainer.

Overridden to add the current population to the trainer’s state.

Return type:: dict

SingleObjPACO._increase_pheromone() → None¶

Increase the amount of pheromone.

Ants entering the population deposit pheromone.

SingleObjPACO._init_internals() → None¶

Set up the trainer internal data structures to start searching.

Create all the internal objects, functions and data structures needed to run the search process. For the SingleObjPACO class, the _pop_ingoing and _pop_outgoing lists of ants for the population are created. Subclasses which need more objects or data structures should override this method.

SingleObjPACO._init_pheromone() → None¶: Init the pheromone matrix(ces) according to the initial value(s).

SingleObjPACO._init_representatives() → None¶

Init the representatives of the other species.

Only used for cooperative approaches, which need representatives of all the species to form a complete solution for the problem. Cooperative subclasses of the Trainer class should override this method to get the representatives of the other species initialized.

SingleObjPACO._init_search() → None¶

Init the search process.

Initialize the state of the trainer (with _init_state()) and all the internal data structures needed (with _init_internals()) to perform the search.

SingleObjPACO._init_state() → None¶

Init the trainer state.

If there is any checkpoint file, the state is initialized from it with the _load_state() method. Otherwise a new initial state is generated with the _new_state() method.

SingleObjPACO._load_state() → None¶

Load the state of the last checkpoint.

Raises:: Exception – If the checkpoint file can’t be loaded

SingleObjPACO._new_state() → None¶

Generate a new trainer state.

Overridden to create an empty population.

SingleObjPACO._next_choice(ant: Ant) → int | None¶

Choose the next node for an ant.

The election is made from the feasible neighborhood of the current node, which is composed of those nodes neither discarded nor visited yet by the ant and connected to its current node.

The best possible node is selected with probability exploitation_prob. In case the best node is not chosen, the next node is selected probabilistically according to the choice_info matrix.

Parameters:: ant (Ant) – The ant
Returns:: The index of the chosen node or None if there isn’t any feasible node
Return type:: int

SingleObjPACO._pheromone_amount(ant: Ant) → tuple[float, ...]¶

Return the amount of pheromone to be deposited by an ant.

All the ants deposit/remove the same amount of pheromone, which is obtained as (initial_pheromone - initial_pheromone) / pop_size.

Parameters:: ant (Ant) – The ant
Returns:: The amount of pheromone to be deposited for each objective
Return type:: tuple[float]

SingleObjPACO._postprocess_iteration() → None¶

Postprocess after doing the iteration.

Subclasses should override this method to make any postprocessment after performing an iteration.

SingleObjPACO._preprocess_iteration() → None¶

Preprocess before doing the iteration.

Subclasses should override this method to make any preprocessment before performing an iteration.

SingleObjPACO._reset_internals() → None¶

Reset the internal structures of the trainer.

Overridden to reset the pop_ingoing and pop_outgoing lists. If subclasses overwrite the _init_internals() method to add any new internal object, this method should also be overridden to reset all the internal objects of the trainer.

SingleObjPACO._reset_state() → None¶

Reset the trainer state.

Overridden to reset the population.

SingleObjPACO._save_state() → None¶

Save the state at a new checkpoint.

Raises:: Exception – If the checkpoint file can’t be written

SingleObjPACO._search() → None¶

Apply the search algorithm.

Execute the trainer until the termination condition is met. Each iteration is composed by the following steps:

SingleObjPACO._set_cooperative_fitness(sol: Solution, fitness_trials_values: [Sequence[tuple[float]]]) → None¶

Estimate a solution fitness from multiple evaluation trials.

Applies an average of the fitness trials values. Trainers requiring another estimation should override this method.

Parameters:

sol (Solution) – The solution
fitness_trials_values (Sequence[tuple[float]]) – Sequence of fitness trials values. Each trial should be obtained with a different context in a cooperative trainer approach.

SingleObjPACO._set_state(state: dict[str, Any]) → None¶

Set the state of this trainer.

Overridden to add the current population to the trainer’s state.

Parameters:: state (dict) – The last loaded state

SingleObjPACO._start_iteration() → None¶

Start an iteration.

Empty the pop_ingoing and pop_outgoing lists.

SingleObjPACO._termination_criterion() → bool¶

Control the search termination.

Returns:: True if either the default termination criterion or a custom termination criterion is met. The default termination criterion is implemented by the _default_termination_func() method. Another custom termination criterion can be set with custom_termination_func method.
Return type:: bool

SingleObjPACO._update_pheromone() → None¶: Update the pheromone trails.

abstract SingleObjPACO._update_pop() → None¶

Update the population.

The population may be updated with the current iteration’s colony, depending on the updation criterion implemented. Ants entering and leaving the population are placed, respectively, in the pop_ingoing and pop_outgoing lists, to be taken into account in the pheromone updation process.

This method should be overridden by subclasses.

Raises:: NotImplementedError – If has not been overridden

culebra.trainer.aco.abc.SingleObjPACO class¶

Class attributes¶

Class methods¶

Properties¶

Private properties¶

Methods¶

Private methods¶

`culebra.trainer.aco.abc.SingleObjPACO` class¶