metaheuristic_designer.initializers.composite_initializer module

class CompositeInitializer(dimension, initializers, weights=None, population_size=None, encoding=None, rng=None)

Bases: Initializer

Methods

generate_individual()	Generate an individual from one of the initializers chosen at random.
generate_population([n_individuals])	Generate a population from individuals chosen at random from the initializers.
generate_random()	Generate a random individual from one initializers chosen at random.
get_state()	Return a minimal dictionary identifying this initializer.

Parameters:

• dimension (int)

• initializers (Iterable[Initializer])

• weights (ndarray[tuple[int], floating] | None)

• population_size (int | None)

• encoding (Encoding)

• rng (int | Generator | None)

generate_random()

Generate a random individual from one initializers chosen at random.

Return type:

ndarray[tuple[int], floating] | ndarray[tuple[int], integer] | ndarray[tuple[int], uint8 | bool]

Returns:

VectorLike

A 1-D array generated by the fallback initializer.

generate_individual()

Generate an individual from one of the initializers chosen at random.

Return type:

ndarray[tuple[int], floating] | ndarray[tuple[int], integer] | ndarray[tuple[int], uint8 | bool]

Returns:

ndarray

A 1-D array representing the individual.

generate_population(n_individuals=None)

Generate a population from individuals chosen at random from the initializers.

Parameters:

objfunc: ObjectiveFunc

Objective function that will be propagated to each individual.

n_individual: int, optional

Number of individuals to generate

Returns:

generated_population: Population

Newly generated population.

class FixedCompositeInitializer(dimension, initializers, amounts=None, population_size=None, encoding=None, rng=None)

Bases: Initializer

Methods

generate_individual()	Generate an individual from one of the initializers chosen deterministically.
generate_population([n_individuals])	Generate a population from individuals chosen at random from the initializers.
generate_random()	Generate a random individual from one of the initializers chosen deterministically.
get_state()	Return a minimal dictionary identifying this initializer.

Parameters:

• dimension (int)

• initializers (Iterable[Initializer])

• amounts (ndarray[tuple[int], integer] | None)

• population_size (int | None)

• encoding (Encoding)

• rng (int | Generator | None)

generate_random()

Generate a random individual from one of the initializers chosen deterministically.

Return type:

ndarray[tuple[int], floating] | ndarray[tuple[int], integer] | ndarray[tuple[int], uint8 | bool]

Returns:

VectorLike

A 1-D array generated by the fallback initializer.

generate_individual()

Generate an individual from one of the initializers chosen deterministically.

Return type:

ndarray[tuple[int], floating] | ndarray[tuple[int], integer] | ndarray[tuple[int], uint8 | bool]

Returns:

ndarray

A 1-D array representing the individual.

generate_population(n_individuals=None)

Generate a population from individuals chosen at random from the initializers.

Parameters:

objfunc: ObjectiveFunc

Objective function that will be propagated to each individual.

n_individual: int, optional

Number of individuals to generate

Returns:

generated_population: Population

Newly generated population.