saealib.GA

class saealib.GA(crossover, mutation, parent_selection, survivor_selection)

Bases: Algorithm

Genetic Algorithm class.

Parameters:

• crossover (Crossover)

• mutation (Mutation)

• parent_selection (ParentSelection)

• survivor_selection (SurvivorSelection)

crossover

Crossover operator.

Type:

Crossover

mutation

Mutation operator.

Type:

Mutation

parent_selection

Parent selection operator.

Type:

ParentSelection

survivor_selection

Survivor selection operator.

Type:

SurvivorSelection

Methods

__init__	Initialize GA (Genetic Algorithm) class.
ask	Generate offspring via crossover and mutation.
create_pareto_archive	Create a ParetoArchive with the correct direction for the problem.
get_required_attrs	Return algorithm-specific attributes (GA needs none beyond the defaults).
tell	Update the population using (μ+λ) survivor selection.

Method Details

GA.__init__(crossover, mutation, parent_selection, survivor_selection)

Initialize GA (Genetic Algorithm) class.

Parameters:

• crossover (Crossover) – Crossover operator.

• mutation (Mutation) – Mutation operator.

• parent_selection (ParentSelection) – Parent selection operator.

• survivor_selection (SurvivorSelection) – Survivor selection operator.

GA.ask(ctx, provider, n_offspring=None)

Generate offspring via crossover and mutation.

Parameters:

• ctx (OptimizationContext) – Current optimization context.

• provider (ComponentProvider) – Component provider.

• n_offspring (int or None, optional) – Number of offspring. Defaults to the current population size.

Return type:

Population

GA.create_pareto_archive(attrs, init_capacity, problem)

Create a ParetoArchive with the correct direction for the problem.

Parameters:

• attrs (list[PopulationAttribute])

• init_capacity (int)

• problem (Problem)

Return type:

ParetoArchive

GA.get_required_attrs(problem)

Return algorithm-specific attributes (GA needs none beyond the defaults).

Parameters:

problem (Problem)

Return type:

list[PopulationAttribute]

GA.tell(ctx, provider, offspring)

Update the population using (μ+λ) survivor selection.

Parameters:

• ctx (OptimizationContext) – Current optimization context.

• provider (ComponentProvider) – Component provider.

• offspring (Population) – Offspring population.