metaheuristic_designer.initializers.uniform_initializer module

Initializer that samples from a uniform distribution.

class UniformInitializer(dimension, lower_bound, upper_bound, population_size=1, encoding=None, dtype=<class 'float'>, rng=None)

Bases: Initializer

Initializer that generates individuals with values drawn from a uniform distribution.

Parameters:

dimensionint

Length of the genotype vector.

lower_boundfloat or array

Lower bound(s) of the distribution. If an array is given, it must have length dimension.

upper_boundfloat or array

Upper bound(s) of the distribution. Must match the shape of lower_bound.

population_sizeint, optional

Number of individuals to generate (default 1).

encodingEncoding, optional

Encoding that will be passed to each individual.

dtypetype, optional

Desired NumPy dtype of the generated vectors (default float).

rngRNGLike, optional

Random number generator.

Methods

generate_individual()	Generate a single individual.
generate_population([n_individuals])	Create a fully formed population of n_individuals individuals.
generate_random()	Generate a single random genotype vector (1-D array).
get_state()	Return a minimal dictionary identifying this initializer.

generate_random()

Generate a single random genotype vector (1-D array).

Returns:

VectorLike

A newly generated genotype vector (1-D array).

generate_individual()

Generate a single individual.

By default simply delegates to generate_random(). Returns a newly generated individual (a 1-D array).

Override this method if your initializer needs to distinguish between a randomly initialize individual and a solution generated with another strategy (See SeedProbInitializer).

Returns:

Any

A newly generated individual.