uo.target_solution package

Submodules

uo.target_solution.evaluation_cache_control_statistics module

The evaluation_cache_control_statistics module describes the class EvaluationCacheControlStatistics.

class uo.solution.evaluation_cache_control_statistics.EvaluationCacheControlStatistics(*args, **kwargs)

Bases: object

Class that represents control statistics for evaluation caching.

property cache: dict[str]

Property getter for cache

Returns:: cache that is used during evaluation
Return type:: dict[str]

property cache_hit_count: int

Property getter for cache_hit_count

Returns:: number of cache hits during evaluation
Return type:: int

property cache_request_count: int

Property getter for cache_request_count

Returns:: overall number of evaluations
Return type:: int

increment_cache_hit_count() → None: Increments number of cache hits during evaluation

increment_cache_request_count() → None: Increments overall number of evaluations

property max_cache_size: int

Property getter for max_cache_size

Returns:: maximum size of the cache - if 0 cache is with unlimited size
Return type:: int

string_rep(delimiter: str, indentation: int = 0, indentation_symbol: str = '', group_start: str = '{', group_end: str = '}') → str

String representation of the EvaluationCacheControlStatistics instance

Parameters:

delimiter (str) – delimiter between fields
indentation (int, optional, default value 0) – level of indentation
indentation_symbol (str, optional, default value '') – indentation symbol
group_start (str, optional, default value '{') – group start string
group_end (str, optional, default value '}') – group end string

Returns:

string representation of instance that controls output

Return type:

str

uo.target_solution.target_solution module

The solution module describes the class Solution.

Bases: Generic[R_co, A_co]

abstract argument(representation: R_co) → A_co

Argument of the target solution

Parameters:: representation (R_co) – internal representation of the solution
Returns:: argument of the solution
Return type:: A_co

calculate_quality(problem: Problem) → QualityOfSolution

Calculate fitness, objective and feasibility of the solution, with optional cache consultation

Parameters:: problem (Problem) – problem that is solved
Returns:: objective value, fitness value and feasibility of the solution instance
Return type:: QualityOfSolution

abstract calculate_quality_directly(representation: R_co, problem: Problem) → QualityOfSolution

Fitness calculation of the target solution

Parameters:

representation (R_co) – native representation of the solution for which objective value, fitness and feasibility are calculated
problem (Problem) – problem that is solved

Returns:

objective value, fitness value and feasibility of the solution instance

Return type:

QualityOfSolution

abstract copy()

Copy the current target solution

Returns:: new uo.solution.Solution instance with the same properties
Return type:: Solution

copy_from(original) → None: Copy all data from the original target solution

evaluate(problem: Problem) → None

Evaluate current target solution

Parameters:: problem (Problem) – problem that is solved

property evaluation_cache_cs: EvaluationCacheControlStatistics | None

Property getter that returns cache for evaluation control and statistics

Returns:: cache for evaluation control and statistics
Return type:: EvaluationCacheControlStatistics

property fitness_value: float

Property getter for fitness value of the target solution

Returns:: fitness value of the target solution instance
Return type:: float

property fitness_values: list[float] | tuple[float]

Property getter for fitness values of the target solution

Returns:: fitness values of the target solution instance
Return type:: list[float]|tuple[float]

abstract init_from(representation: R_co, problem: Problem) → None

Initialization of the solution, by setting its native representation

Parameters:

representation (R_co) – representation that will be ste to solution
problem (Problem) – problem which is solved by solution

abstract init_random(problem: Problem) → None

Random initialization of the solution

Parameters:: problem (Problem) – problem which is solved by solution

is_better(other: Solution, problem: Problem) → bool | None

Checks if this solution is better than the other, with respect to problem that is optimized

Parameters:

other (Solution) – other solution
problem (Problem) – problem to be solved

Returns:

True if first is better, False if first is worse, None if quality of both solutions are equal

Return type:

bool

property is_feasible: bool

Property getter for feasibility of the target solution

Returns:: feasibility of the target solution instance
Return type:: bool

abstract native_representation(representation_str: str) → R_co

Obtain native representation from solution code of the Solution instance

Parameters:: representation_str (str) – solution’s representation as string (e.g. solution code)
Returns:: solution’s native representation
Return type:: R_co

property objective_value: float

Property getter for objective value of the target solution

Returns:: objective value of the target solution instance
Return type:: float

property objective_values: list[float] | tuple[float]

Property getter for objective values of the target solution

Returns:: objective values of the target solution instance
Return type:: list[float]|tuple[float]

obtain_feasible_representation(problem: Problem) → R_co

property quality: QualityOfSolution

Property getter for the quality of the target solution

Returns:: quality of the target solution, in the single-objective optimization context
Return type:: QualityOfSolution

property random_seed: int

Property getter for the random seed used during metaheuristic execution

Returns:: random seed
Return type:: int

property representation: R_co

Property getter for representation of the target solution

Returns:: representation of the target solution instance
Return type:: R_co

representation_distance(representation_1: R_co, representation_2: R_co) → float

Calculate distance between two native representations, with optional cache consultation

Parameters:

representation_1 (R_co) – native representation for the first solution
representation_2 (R_co) – native representation for the second solution

Returns:

distance

Return type:

float

property representation_distance_cache_cs: DistanceCalculationCacheControlStatistics | None

Property getter that returns cache for distance calculation control and statistics

Returns:: cache for distance calculation control and statistics
Return type:: DistanceCalculationCacheControlStatistics

abstract representation_distance_directly(representation_1: R_co, representation_2: R_co) → float

Directly calculate distance between two solutions determined by its native representations

Parameters:

representation_1 (R_co) – native representation for the first solution
representation_2 (R_co) – native representation for the second solution

Returns:

distance

Return type:

float

string_rep(delimiter: str, indentation: int = 0, indentation_symbol: str = '', group_start: str = '{', group_end: str = '}') → str

String representation of the target solution instance

Parameters:

delimiter (str) – delimiter between fields
indentation (int, optional, default value 0) – level of indentation
indentation_symbol (str, optional, default value '') – indentation symbol
group_start (str, optional, default value '{') – group start string
group_end (str, optional, default value '}') – group end string

Returns:

string representation of instance that controls output

Return type:

str

string_representation() → str

String representation of the target solution

Parameters:: representation (R_co) – internal representation of the solution
Returns:: string representation of the solution
Return type:: str

uo.solution.solution.random() → x in the interval [0, 1).

uo.target_solution package

Submodules

uo.target_solution.evaluation_cache_control_statistics module

uo.target_solution.target_solution module

Module contents