metaheuristic_designer.initializers.gaussian_initializer module#

Initializer that samples from a Gaussian (normal) distribution.

class GaussianInitializer(dimension, g_mean, g_std, pop_size=1, encoding=None, dtype=<class 'float'>, rng=None)[source]#

Initializer that generates individuals with values drawn from a Gaussian (normal) distribution.

Parameters:

dimensionint: Length of the genotype vector.
g_meanfloat or array: Mean of the distribution. If an array is given, it must have length dimension.
g_stdfloat or array: Standard deviation of the distribution. If an array is given, it must have length dimension.
pop_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()[source]#

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

Returns: