geneticengine.algorithms.gp.parameterless

Attributes

T

Classes

`ParameterlessPopulationInitializer`
`RandomizeParallelStep`
`RegenerateWeightsStep`
`GenericAdaptiveCrossoverStep`
`InitiallyRandomGeneticProgramming`	A Genetic Programming version that uses random configurations, set before the evolution.
`AlwaysRandomGeneticProgramming`	A Genetic Programming version that uses random configurations, which are regenerated every generation.

Functions

`generate_random_population_size`(random)
`time_for_initialization`(budget)
`best_of_population`(population, problem)

Module Contents

geneticengine.algorithms.gp.parameterless.generate_random_population_size(random)

Parameters:: random (geneticengine.random.sources.RandomSource)
Return type:: int

geneticengine.algorithms.gp.parameterless.time_for_initialization(budget)

Parameters:: budget (geneticengine.evaluation.budget.SearchBudget)
Return type:: Optional[float]

class geneticengine.algorithms.gp.parameterless.ParameterlessPopulationInitializer(budget, tracker)

Bases: geneticengine.algorithms.gp.structure.PopulationInitializer

Parameters:

budget (geneticengine.evaluation.budget.SearchBudget)
tracker (geneticengine.evaluation.tracker.ProgressTracker)

budget

tracker

initialize(problem, representation, random, target_size, **kwargs)

Parameters:

problem (geneticengine.problems.Problem)
representation (geneticengine.representations.api.Representation)
random (geneticengine.random.sources.RandomSource)
target_size (int)

Return type:

Iterator[geneticengine.solutions.individual.PhenotypicIndividual]

class geneticengine.algorithms.gp.parameterless.RandomizeParallelStep

Bases: geneticengine.algorithms.gp.operators.combinators.ParallelStep

post_iterate(problem, evaluator, representation, random, population, target_size, generation)

Parameters:

problem (geneticengine.problems.Problem)
evaluator (geneticengine.evaluation.api.Evaluator)
representation (geneticengine.representations.api.Representation)
random (geneticengine.random.sources.RandomSource)
population (Iterator[geneticengine.solutions.individual.PhenotypicIndividual])
target_size (int)
generation (int)

Return type:

None

geneticengine.algorithms.gp.parameterless.T

geneticengine.algorithms.gp.parameterless.best_of_population(population, problem)

Parameters:

population (Iterator[T])
problem (geneticengine.problems.Problem)

Return type:

geneticengine.solutions.individual.Individual

class geneticengine.algorithms.gp.parameterless.RegenerateWeightsStep(mut, xo, tmut, txo)

Bases: geneticengine.algorithms.gp.operators.combinators.IdentityStep

Parameters:

mut

xo

tmut

txo

post_iterate(problem, evaluator, representation, random, population, target_size, generation)

Parameters:

problem (geneticengine.problems.Problem)
evaluator (geneticengine.evaluation.api.Evaluator)
representation (geneticengine.representations.api.Representation)
random (geneticengine.random.sources.RandomSource)
population (Iterator[geneticengine.solutions.individual.PhenotypicIndividual])
target_size (int)
generation (int)

Return type:

None

class geneticengine.algorithms.gp.parameterless.GenericAdaptiveCrossoverStep(probability=1)

Bases: geneticengine.algorithms.gp.operators.crossover.GenericCrossoverStep

Parameters:: probability (float)

last_fitness: geneticengine.problems.Fitness

first = True

post_iterate(problem, evaluator, representation, random, population, target_size, generation)

Parameters:

problem (geneticengine.problems.Problem)
evaluator (geneticengine.evaluation.api.Evaluator)
representation (geneticengine.representations.api.Representation)
random (geneticengine.random.sources.RandomSource)
population (Iterator[geneticengine.solutions.individual.PhenotypicIndividual])
target_size (int)
generation (int)

Return type:

None

class geneticengine.algorithms.gp.parameterless.InitiallyRandomGeneticProgramming(problem, budget, representation, random=None, tracker=None)

Bases: geneticengine.algorithms.gp.gp.GeneticProgramming

A Genetic Programming version that uses random configurations, set before the evolution.

Parameters:

problem (geneticengine.problems.Problem)
budget (geneticengine.evaluation.budget.SearchBudget)
representation (geneticengine.representations.api.Representation)
random (geneticengine.random.sources.RandomSource)
tracker (geneticengine.evaluation.tracker.ProgressTracker | None)

population_initializer

population_size

mutation_tournament

mutation

crossover_tournament

crossover

step

class geneticengine.algorithms.gp.parameterless.AlwaysRandomGeneticProgramming(problem, budget, representation, random=None, tracker=None)

Bases: geneticengine.algorithms.gp.gp.GeneticProgramming

A Genetic Programming version that uses random configurations, which are regenerated every generation.

Parameters:

problem (geneticengine.problems.Problem)
budget (geneticengine.evaluation.budget.SearchBudget)
representation (geneticengine.representations.api.Representation)
random (geneticengine.random.sources.RandomSource)
tracker (geneticengine.evaluation.tracker.ProgressTracker | None)

population_initializer

population_size

mutation_tournament

mutation

crossover_tournament

crossover

step