metaheuristic_designer.initializers package¶

Submodules¶

metaheuristic_designer.initializers.direct_initializer module¶

Initializer that uses a set of predefined solutions as the first generation.

Bases: Initializer

Initializer that seeds the population with a given set of solutions.

If the number of individuals requested exceeds the size of the stored set, individuals are cycled through. Random individuals from a fallback initializer are used when generate_random() is called directly.

Parameters:

default_init (Initializer) – Fallback initializer for generate_random().
solutions (Population, list or ndarray) – The set of solutions to draw from.
encoding (Encoding, optional) – Encoding attached to the population (used when solutions is a Population).
random_state (RNGLike, optional) – Random number generator.

generate_random() → ndarray[tuple[int], floating] | ndarray[tuple[int], integer] | ndarray[tuple[int], uint8 | bool][source]¶

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

Returns:: A newly generated genotype vector (1-D array).
Return type:: VectorLike

generate_individual() → ndarray[tuple[int], floating] | ndarray[tuple[int], integer] | ndarray[tuple[int], uint8 | bool][source]¶

Return a randomly chosen individual from the stored solution set.

Returns:: A 1-D array taken from the predefined solutions.
Return type:: VectorLike

generate_population(objfunc: ObjectiveFunc, n_individuals: int | None = None) → Population[source]¶

Create a population by drawing from the stored solutions.

Parameters:

objfunc (ObjectiveFunc) – The objective function.
n_individuals (int, optional) – Number of individuals to generate. Defaults to population_size.

Returns:

A population built from the predefined solutions.

Return type:

Population

metaheuristic_designer.initializers.exponential_initializer module¶

Initializer that samples from an exponential distribution.

class ExponentialInitializer(dimension, beta, pop_size=1, encoding=None, dtype=<class 'float'>, random_state=None)[source]¶

Bases: Initializer

Initializer that generates individuals with values drawn from an exponential distribution.

Parameters:

dimension (int) – Length of the genotype vector.
beta (float or array) – Scale parameter of the exponential distribution (1 / rate).
pop_size (int, optional) – Number of individuals to generate (default 1).
encoding (Encoding, optional) – Encoding that will be passed to each individual.
dtype (type, optional) – Desired NumPy dtype of the generated vectors (default float).
random_state (RNGLike, optional) – Random number generator.

generate_random()[source]¶

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

Returns:: A newly generated genotype vector (1-D array).
Return type:: VectorLike

generate_individual()[source]¶

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:: A newly generated individual.
Return type:: Any

metaheuristic_designer.initializers.extended_initializer module¶

Initializer for genotypes that contain both a solution and extra parameters.

class ExtendedInitializer(solution_init: Initializer, param_init_dict: dict, encoding: ParameterExtendingEncoding, random_state=None)[source]¶

Bases: Initializer

Initializer that combines a solution initializer with one or more parameter initializers.

This is used with ParameterExtendingEncoding to produce genotypes that store extra information (e.g., velocity for PSO, mutation strengths for self-adaptation).

Parameters:

solution_init (Initializer) – Initializer for the solution part of the genotype.
param_init_dict (dict) – Mapping of parameter names to their corresponding initializers.
encoding (ParameterExtendingEncoding) – The extended encoding that defines the parameter layout.
random_state (RNGLike, optional) – Random number generator.

generate_random()[source]¶

Generate a random genotype vector with solution and parameter parts.

Returns:: A 1-D array with the solution followed by the extra parameters.
Return type:: ndarray

generate_individual()[source]¶

Generate an individual (by default identical to generate_random()).

Returns:: A 1-D array with the solution and parameter parts.
Return type:: ndarray

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'>, random_state=None)[source]¶

Bases: Initializer

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

Parameters:

dimension (int) – Length of the genotype vector.
g_mean (float or array) – Mean of the distribution. If an array is given, it must have length dimension.
g_std (float or array) – Standard deviation of the distribution. If an array is given, it must have length dimension.
pop_size (int, optional) – Number of individuals to generate (default 1).
encoding (Encoding, optional) – Encoding that will be passed to each individual.
dtype (type, optional) – Desired NumPy dtype of the generated vectors (default float).
random_state (RNGLike, optional) – Random number generator.

generate_random()[source]¶

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

Returns:: A newly generated genotype vector (1-D array).
Return type:: VectorLike

generate_individual()[source]¶

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:: A newly generated individual.
Return type:: Any

metaheuristic_designer.initializers.perm_initializer module¶

Initializer that generates random permutations.

class PermInitializer(dimension: int, population_size: int = 1, encoding: Encoding | None = None, random_state: int | Generator | None = None)[source]¶

Bases: Initializer

Initializer that generates individuals as random permutations of integers 0, 1, …, dimension-1.

Parameters:

dimension (int) – Length of the permutation (number of elements).
pop_size (int, optional) – Number of individuals to generate (default 1).
encoding (Encoding, optional) – Encoding that will be passed to each individual.
random_state (RNGLike, optional) – Random number generator.

generate_random()[source]¶

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

Returns:: A newly generated genotype vector (1-D array).
Return type:: VectorLike

metaheuristic_designer.initializers.seed_initializer module¶

Initializers that insert predefined solutions into the population.

Bases: Initializer

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_init (Initializer) – Fallback initializer for random individuals.
solutions (Population, Iterable[VectorLike] or MatrixLike) – Set of predefined solutions to draw from.
insert_prob (float, optional) – Probability of using a predefined solution (default 0.1).
random_state (RNGLike, optional) – Random number generator.

generate_random() → ndarray[tuple[int], floating] | ndarray[tuple[int], integer] | ndarray[tuple[int], uint8 | bool][source]¶

Generate a random individual using the fallback initializer.

Returns:: A 1-D array generated by the fallback initializer.
Return type:: VectorLike

generate_individual() → ndarray[tuple[int], floating] | ndarray[tuple[int], integer] | ndarray[tuple[int], uint8 | bool][source]¶

Generate an individual, possibly replacing it with a seeded solution.

With probability insert_prob a randomly chosen predefined solution is returned; otherwise a new random individual is created by the fallback initializer.

Returns:: A 1-D array representing the individual.
Return type:: ndarray

Bases: Initializer

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_init (Initializer) – Fallback initializer for random individuals.
solutions (Population, Iterable[VectorLike] or MatrixLike) – Set of predefined solutions to draw from.
n_to_insert (int, optional) – Exact number of predefined solutions to insert. Defaults to the size of the solution set.
random_state (RNGLike, optional) – Random number generator.

generate_random() → ndarray[tuple[int], floating] | ndarray[tuple[int], integer] | ndarray[tuple[int], uint8 | bool][source]¶

Generate a random individual using the fallback initializer.

Returns:: A 1-D array generated by the fallback initializer.
Return type:: VectorLike

generate_individual() → ndarray[tuple[int], floating] | ndarray[tuple[int], integer] | ndarray[tuple[int], uint8 | bool][source]¶

Return a predefined solution until the insertion quota is met.

The first n_to_insert individuals are taken from the solution set (cycling if necessary). After that, random individuals are generated by the fallback initializer.

Returns:: A 1-D array representing the individual.
Return type:: VectorLike

generate_population(objfunc, n_individuals: int | None = None) → Population[source]¶

Create the population, resetting the insertion counter first.

Parameters:

objfunc (ObjectiveFunc) – The objective function.
n_individuals (int, optional) – Number of individuals to generate. Defaults to population_size.

Returns:

A population with the predefined solutions inserted at the beginning.

Return type:

Population

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'>, random_state=None)[source]¶

Bases: Initializer

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

Parameters:

dimension (int) – Length of the genotype vector.
lower_bound (float or array) – Lower bound(s) of the distribution. If an array is given, it must have length dimension.
upper_bound (float or array) – Upper bound(s) of the distribution. Must match the shape of lower_bound.
population_size (int, optional) – Number of individuals to generate (default 1).
encoding (Encoding, optional) – Encoding that will be passed to each individual.
dtype (type, optional) – Desired NumPy dtype of the generated vectors (default float).
random_state (RNGLike, optional) – Random number generator.

generate_random()[source]¶

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

Returns:: A newly generated genotype vector (1-D array).
Return type:: VectorLike

generate_individual()[source]¶

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:: A newly generated individual.
Return type:: Any

Module contents¶

Concrete encoding implementations provided by the library.

Bases: Initializer

Initializer that seeds the population with a given set of solutions.

If the number of individuals requested exceeds the size of the stored set, individuals are cycled through. Random individuals from a fallback initializer are used when generate_random() is called directly.

Parameters:

default_init (Initializer) – Fallback initializer for generate_random().
solutions (Population, list or ndarray) – The set of solutions to draw from.
encoding (Encoding, optional) – Encoding attached to the population (used when solutions is a Population).
random_state (RNGLike, optional) – Random number generator.

generate_random() → ndarray[tuple[int], floating] | ndarray[tuple[int], integer] | ndarray[tuple[int], uint8 | bool][source]¶

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

Returns:: A newly generated genotype vector (1-D array).
Return type:: VectorLike

generate_individual() → ndarray[tuple[int], floating] | ndarray[tuple[int], integer] | ndarray[tuple[int], uint8 | bool][source]¶

Return a randomly chosen individual from the stored solution set.

Returns:: A 1-D array taken from the predefined solutions.
Return type:: VectorLike

generate_population(objfunc: ObjectiveFunc, n_individuals: int | None = None) → Population[source]¶

Create a population by drawing from the stored solutions.

Parameters:

objfunc (ObjectiveFunc) – The objective function.
n_individuals (int, optional) – Number of individuals to generate. Defaults to population_size.

Returns:

A population built from the predefined solutions.

Return type:

Population

class ExponentialInitializer(dimension, beta, pop_size=1, encoding=None, dtype=<class 'float'>, random_state=None)[source]¶

Bases: Initializer

Initializer that generates individuals with values drawn from an exponential distribution.

Parameters:

dimension (int) – Length of the genotype vector.
beta (float or array) – Scale parameter of the exponential distribution (1 / rate).
pop_size (int, optional) – Number of individuals to generate (default 1).
encoding (Encoding, optional) – Encoding that will be passed to each individual.
dtype (type, optional) – Desired NumPy dtype of the generated vectors (default float).
random_state (RNGLike, optional) – Random number generator.

generate_random()[source]¶

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

Returns:: A newly generated genotype vector (1-D array).
Return type:: VectorLike

generate_individual()[source]¶

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:: A newly generated individual.
Return type:: Any

class ExtendedInitializer(solution_init: Initializer, param_init_dict: dict, encoding: ParameterExtendingEncoding, random_state=None)[source]¶

Bases: Initializer

Initializer that combines a solution initializer with one or more parameter initializers.

This is used with ParameterExtendingEncoding to produce genotypes that store extra information (e.g., velocity for PSO, mutation strengths for self-adaptation).

Parameters:

solution_init (Initializer) – Initializer for the solution part of the genotype.
param_init_dict (dict) – Mapping of parameter names to their corresponding initializers.
encoding (ParameterExtendingEncoding) – The extended encoding that defines the parameter layout.
random_state (RNGLike, optional) – Random number generator.

generate_random()[source]¶

Generate a random genotype vector with solution and parameter parts.

Returns:: A 1-D array with the solution followed by the extra parameters.
Return type:: ndarray

generate_individual()[source]¶

Generate an individual (by default identical to generate_random()).

Returns:: A 1-D array with the solution and parameter parts.
Return type:: ndarray

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

Bases: Initializer

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

Parameters:

dimension (int) – Length of the genotype vector.
g_mean (float or array) – Mean of the distribution. If an array is given, it must have length dimension.
g_std (float or array) – Standard deviation of the distribution. If an array is given, it must have length dimension.
pop_size (int, optional) – Number of individuals to generate (default 1).
encoding (Encoding, optional) – Encoding that will be passed to each individual.
dtype (type, optional) – Desired NumPy dtype of the generated vectors (default float).
random_state (RNGLike, optional) – Random number generator.

generate_random()[source]¶

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

Returns:: A newly generated genotype vector (1-D array).
Return type:: VectorLike

generate_individual()[source]¶

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:: A newly generated individual.
Return type:: Any

class Initializer(dimension: int, population_size: int = 1, encoding: Encoding | None = None, random_state: int | Generator | None = None)[source]¶

Bases: ABC

Abstract base for all population initializers.

An initializer creates the first generation of individuals. It must provide a way to generate a single random genotype vector (a 1-D NumPy array) via generate_random() and can optionally wrap it with a different definition of an individual via generate_individual().

Parameters:

dimension (int) – Length of the genotype vector.
population_size (int, optional) – Number of individuals to generate (default 1).
encoding (Encoding, optional) – Encoding that will be attached to every individual. Defaults to DefaultEncoding.
random_state (RNGLike, optional) – Random number generator.

abstract generate_random() → ndarray[tuple[int], floating] | ndarray[tuple[int], integer] | ndarray[tuple[int], uint8 | bool][source]¶

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

Returns:: A newly generated genotype vector (1-D array).
Return type:: VectorLike

generate_individual() → ndarray[tuple[int], floating] | ndarray[tuple[int], integer] | ndarray[tuple[int], uint8 | bool][source]¶

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:: A newly generated individual.
Return type:: Any

generate_population(objfunc: ObjectiveFunc, n_individuals: int | None = None) → Population[source]¶

Create a fully formed population of n_individuals individuals.

Parameters:

objfunc (ObjectiveFunc) – Objective function that will be propagated to each individual.
n_individual (int, optional) – Number of individuals to generate

Returns:

generated_population – Newly generated population.

Return type:

Population

get_state() → dict[source]¶

Return a minimal dictionary identifying this initializer.

Returns:: Dictionary with key "class_name".
Return type:: dict

class InitializerFromLambda(generator: Callable, dimension: int, pop_size: int = 1, encoding: Encoding | None = None, random_state: int | Generator | None = None)[source]¶

Bases: Initializer

Initializer that uses a user-provided function to generate individuals.

Parameters:

generator (callable) – A function (random_state) -> genotype that returns a single genotype vector.
dimension (int) – Length of the genotype vector.
pop_size (int, optional) – Number of individuals to generate (default 1).
encoding (Encoding, optional) – Encoding attached to every individual.
random_state (RNGLike, optional) – Random number generator.

generate_random() → ndarray[tuple[int], floating] | ndarray[tuple[int], integer] | ndarray[tuple[int], uint8 | bool][source]¶

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

Returns:: A newly generated genotype vector (1-D array).
Return type:: VectorLike

class PermInitializer(dimension: int, population_size: int = 1, encoding: Encoding | None = None, random_state: int | Generator | None = None)[source]¶

Bases: Initializer

Initializer that generates individuals as random permutations of integers 0, 1, …, dimension-1.

Parameters:

dimension (int) – Length of the permutation (number of elements).
pop_size (int, optional) – Number of individuals to generate (default 1).
encoding (Encoding, optional) – Encoding that will be passed to each individual.
random_state (RNGLike, optional) – Random number generator.

generate_random()[source]¶

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

Returns:: A newly generated genotype vector (1-D array).
Return type:: VectorLike

Bases: Initializer

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_init (Initializer) – Fallback initializer for random individuals.
solutions (Population, Iterable[VectorLike] or MatrixLike) – Set of predefined solutions to draw from.
n_to_insert (int, optional) – Exact number of predefined solutions to insert. Defaults to the size of the solution set.
random_state (RNGLike, optional) – Random number generator.

generate_random() → ndarray[tuple[int], floating] | ndarray[tuple[int], integer] | ndarray[tuple[int], uint8 | bool][source]¶

Generate a random individual using the fallback initializer.

Returns:: A 1-D array generated by the fallback initializer.
Return type:: VectorLike

generate_individual() → ndarray[tuple[int], floating] | ndarray[tuple[int], integer] | ndarray[tuple[int], uint8 | bool][source]¶

Return a predefined solution until the insertion quota is met.

The first n_to_insert individuals are taken from the solution set (cycling if necessary). After that, random individuals are generated by the fallback initializer.

Returns:: A 1-D array representing the individual.
Return type:: VectorLike

generate_population(objfunc, n_individuals: int | None = None) → Population[source]¶

Create the population, resetting the insertion counter first.

Parameters:

objfunc (ObjectiveFunc) – The objective function.
n_individuals (int, optional) – Number of individuals to generate. Defaults to population_size.

Returns:

A population with the predefined solutions inserted at the beginning.

Return type:

Population

Bases: Initializer

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_init (Initializer) – Fallback initializer for random individuals.
solutions (Population, Iterable[VectorLike] or MatrixLike) – Set of predefined solutions to draw from.
insert_prob (float, optional) – Probability of using a predefined solution (default 0.1).
random_state (RNGLike, optional) – Random number generator.

generate_random() → ndarray[tuple[int], floating] | ndarray[tuple[int], integer] | ndarray[tuple[int], uint8 | bool][source]¶

Generate a random individual using the fallback initializer.

Returns:: A 1-D array generated by the fallback initializer.
Return type:: VectorLike

generate_individual() → ndarray[tuple[int], floating] | ndarray[tuple[int], integer] | ndarray[tuple[int], uint8 | bool][source]¶

Generate an individual, possibly replacing it with a seeded solution.

With probability insert_prob a randomly chosen predefined solution is returned; otherwise a new random individual is created by the fallback initializer.

Returns:: A 1-D array representing the individual.
Return type:: ndarray

class UniformInitializer(dimension, lower_bound, upper_bound, population_size=1, encoding=None, dtype=<class 'float'>, random_state=None)[source]¶

Bases: Initializer

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

Parameters:

dimension (int) – Length of the genotype vector.
lower_bound (float or array) – Lower bound(s) of the distribution. If an array is given, it must have length dimension.
upper_bound (float or array) – Upper bound(s) of the distribution. Must match the shape of lower_bound.
population_size (int, optional) – Number of individuals to generate (default 1).
encoding (Encoding, optional) – Encoding that will be passed to each individual.
dtype (type, optional) – Desired NumPy dtype of the generated vectors (default float).
random_state (RNGLike, optional) – Random number generator.

generate_random()[source]¶

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

Returns:: A newly generated genotype vector (1-D array).
Return type:: VectorLike

generate_individual()[source]¶

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:: A newly generated individual.
Return type:: Any