metaheuristic_designer.initializers.seed_initializer module#

Initializers that insert predefined solutions into the population.

class SeededInitializer(default_init, solutions, insert_prob=0.1, population_size=None, rng=None)[source]#

Bases: CompositeInitializer

Initializer that inserts a predefined solution with a given probability.

With probability insert_prob, a randomly chosen solution from the provided set is used; otherwise a random individual is generated by the fallback initializer.

Parameters:
default_initInitializer

Fallback initializer for random individuals.

solutionsPopulation, Iterable[VectorLike] or MatrixLike

Set of predefined solutions to draw from.

insert_probfloat, optional

Probability of using a predefined solution (default 0.1).

rngRNGLike, optional

Random number generator.

Parameters:

  • default_init (Initializer)

  • solutions (Population | Iterable[VectorLike] | MatrixLike)

  • insert_prob (float)

  • population_size (int)

  • rng (Optional[RNGLike])

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.
class FixedSeededInitializer(default_init, solutions, n_to_insert=None, population_size=None, rng=None)[source]#

Bases: FixedCompositeInitializer

Initializer that inserts a fixed number of predefined solutions.

The first n_to_insert individuals generated are taken from the solution set (cycled if necessary); the remaining are created by the fallback initializer.

Parameters:
default_initInitializer

Fallback initializer for random individuals.

solutionsPopulation, Iterable[VectorLike] or MatrixLike

Set of predefined solutions to draw from.

n_to_insertint, optional

Exact number of predefined solutions to insert. Defaults to the size of the solution set.

rngRNGLike, optional

Random number generator.

Parameters:

  • default_init (Initializer)

  • solutions (Population | Iterable[VectorLike] | MatrixLike)

  • n_to_insert (int)

  • population_size (int)

  • rng (Optional[RNGLike])

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.