Tools

Operators

fevovaq.tools.operators.cx_blx_alpha(parents, cx_mask, xp, alpha=0.5)

BLX-alpha crossover.

Parameters:

• parents – Parents as arrays of real parameters with (pop_size,`n_params`) shape.

• cx_mask – Crossover mask.

• xp – Numpy or Cupy backend.

• alpha – Positive real hyperparameter.

Returns:

Resulting offspring.

fevovaq.tools.operators.cx_one_point(parents, cx_mask, xp)

One-point crossover.

Parameters:

• parents – Parents as arrays of real parameters with (pop_size,`n_params`) shape.

• cx_mask – Crossover mask.

• xp – Numpy or Cupy backend.

Returns:

Resulting offspring.

fevovaq.tools.operators.cx_two_point(parents, cx_mask, xp)

Two-point crossover.

Parameters:

• parents – Parents as arrays of real parameters with (pop_size,`n_params`) shape.

• cx_mask – Crossover mask.

• xp – Numpy or Cupy backend.

Returns:

Resulting offspring.

fevovaq.tools.operators.cx_uniform(parents, cx_mask, xp, cx_indpb=0.5)

Uniform crossover.

Parameters:

• parents – Parents as arrays of real parameters with (pop_size,`n_params`) shape.

• cx_mask – Crossover mask.

• xp – Numpy or Cupy backend.

• cx_indpb – Independent probability for each parameter to be exchanged.

Returns:

Resulting offspring.

fevovaq.tools.operators.mut_gaussian(population, mut_mask, xp, mu=0, sigma=1, mut_indpb=0.1)

Gaussian mutation.

Parameters:

• population – A population of individuals as array of real parameters with (pop_size, n_params) shape.

• mut_mask – Mask of mutation.

• xp – Numpy or Cupy backend.

• mu – Mean or sequence of means for the gaussian addition mutation.

• sigma – Standard deviation or sequence of standard deviations for the gaussian addition mutation.

• mut_indpb – Independent probability for each parameter to be mutated.

Returns:

Resulting mutated population.

fevovaq.tools.operators.sel_best(population, fitness, k, xp)

Selection of the first k best individuals (with the smallest objective values) in the population.

Parameters:

• population – A population of individuals as array of real parameters with (pop_size, n_params) shape.

• fitness – A set of fitness values associated to the population as array of real values with (pop_size, ) shape.

• k – Number of individuals to be selected.

• xp – Numpy or Cupy backend.

Returns:

Indices of selected individuals.

fevovaq.tools.operators.sel_permutation(population, fitness, xp)

Selection of the individuals by permuting the population.

Parameters:

• population – A population of individuals as array of real parameters with (pop_size, n_params) shape.

• fitness – A set of fitness values associated to the population as array of real values with (pop_size, ) shape.

• xp – Numpy or Cupy backend.

Returns:

Indices of selected individuals.

fevovaq.tools.operators.sel_random(population, fitness, k, xp, replace=True)

Selection of k individuals randomly.

Parameters:

• population – A population of individuals as array of real parameters with (pop_size, n_params) shape.

• fitness – A set of fitness values associated to the population as array of real values with (pop_size, ) shape.

• k – Number of individuals to be selected.

• xp – Numpy or Cupy backend.

• replace – Whether the sample is with or without replacement. Default is True, meaning that an element can be selected multiple times.

Returns:

Indices of selected individuals.

fevovaq.tools.operators.sel_tournament(population, fitness, k, xp, tournament_size=3)

Selection of the best individual among tournament_size randomly chosen individuals, k times.

Parameters:

• population – A population of individuals as array of real parameters with (pop_size, n_params) shape.

• fitness – A set of fitness values associated to the population as array of real values with (pop_size, ) shape.

• k – Number of individuals to be selected.

• xp – Numpy or Cupy backend.

• tournament_size – Size of the tournament.

Returns:

Indices of selected individuals.

Distances

fevovaq.tools.distances.fitness_l1(population, fitness, xp)

fevovaq.tools.distances.fitness_l2(population, fitness, xp)

fevovaq.tools.distances.fitness_relative(population, fitness, xp, eps=1e-12)

fevovaq.tools.distances.hybrid_param_fit(population, fitness, xp, fact=0.5)

fevovaq.tools.distances.param_l1(population, fitness, xp)

fevovaq.tools.distances.param_l2(population, fitness, xp)

Support facilities

class fevovaq.tools.support.BestIndividualTracker(xp)

Bases: object

Class used to track the best solution ever found during the algorithm execution.

Parameters:

xp – Numpy or Cupy backend.

get_best()

Get the best individual ever found.

get_best_fit()

Get the best fitness value ever found.

update(population, fitness)

Update the tracker.

Parameters:

• population – A population of individuals as array of real parameters with (pop_size, n_params) shape.

• fitness – A set of fitness values associated to the population as array of real values with (pop_size, ) shape.

class fevovaq.tools.support.FinalResult

Bases: dict

Class used to store the final result.

class fevovaq.tools.support.Logbook

Bases: dict

Class used to store info during the evolution.

get_log()

Get the logbook.

record(**infos)

Record info.

Parameters:

infos – Info to be stored defined in a dictionary.

fevovaq.tools.support.compute_statistics(fitness, xp) → dict

Compute the statistics of fitness values.

Parameters:

• fitness – Fitness values as an array of real values with (pop_size, ) shape.

• xp –

  Numpy or Cupy backend.

  Returns:

  Dictionary containing the min, max, mean, and standard deviation value.

fevovaq.tools.support.print_info(n_run: int, header: bool = False, **kwargs)

Print info.

Parameters:

• n_run – Independent execution number of the algorithm.

• header – If True, the string indicating the independent execution number is printed.

• kwargs – Information to be printed defined in a dictionary.

fevovaq.tools.support.set_progress_bar(max_gen: int, max_nfev: int | None, desc: str | None = None, unit: str | None = None) → tqdm

Set the progress bar based on the stopping criterion.

Parameters:

• max_gen – Maximum number of generations.

• max_nfev – Maximum number of fitness evaluations.

• desc – Description related to the stopping criterion.

• unit – Unit describing the progress.

Returns:

The progress bar.