metaheuristic_designer.operators.operator_functions package

Submodules

• metaheuristic_designer.operators.operator_functions.crossover module
  • random_split()
  • stable_split()
  • create_pairing_fn()
  • k_point_crossover()
  • uniform_crossover()
  • averaged_crossover()
  • blend_crossover()
  • sbx_crossover()
  • bitwise_xor_crossover()
  • multiparent_discrete_crossover()
  • multiparent_intermediate_crossover()
• metaheuristic_designer.operators.operator_functions.differential_evolution module
  • differential_evolution_rand1()
  • differential_evolution_best1()
  • differential_evolution_rand2()
  • differential_evolution_best2()
  • differential_evolution_current_to_rand1()
  • differential_evolution_current_to_best1()
  • differential_evolution_current_to_pbest1()
• metaheuristic_designer.operators.operator_functions.mutation module
  • mutate_sample()
  • mutate_noise()
  • rand_sample()
  • rand_noise()
  • sample_1_sigma()
  • mutate_1_sigma()
  • mutate_n_sigmas()
  • xor_mask()
  • polynomial_mutation()
• metaheuristic_designer.operators.operator_functions.permutation module
  • permute_mutation()
  • roll_mutation()
  • invert_mutation()
  • pmx()
  • pmx_single()
  • order_cross()
  • order_cross_single()
• metaheuristic_designer.operators.operator_functions.probability_distributions module
  • Distribution
    • Distribution.sample()
    • Distribution.estimate_parameters()
  • ScipyUnivarDistribution
    • ScipyUnivarDistribution.sample()
  • ScipyMultivarDistribution
    • ScipyMultivarDistribution.sample()
  • multivariate_categorical
    • multivariate_categorical.sample()
  • uniform_param_fix()
  • normal_heuristic()
  • uniform_heuristic()
  • cauchy_heuristic()
  • laplace_heuristic()
  • gamma_heuristic()
  • expon_heuristic()
  • levy_stable_heuristic()
  • poisson_heuristic()
  • bernoulli_heuristic()
  • binomial_heuristic()
  • tikhinov_heuristic()
  • multivariate_normal_heuristic()
  • dirichlet_heuristic()
  • tikhinov_fisher_heuristic()
• metaheuristic_designer.operators.operator_functions.probability_distributions_factory module
  • create_prob_distribution()
  • add_distribution_entry()
  • list_distributions()
• metaheuristic_designer.operators.operator_functions.random_generation module
  • compute_statistic()
  • random_initialize()
  • random_reset()
• metaheuristic_designer.operators.operator_functions.swarm module
  • pso_operator()
  • pso_operator_wrapper()
• metaheuristic_designer.operators.operator_functions.utils module
  • dummy_op()
  • add_const()
  • OperatorFnDef
    • OperatorFnDef.operator_fn
    • OperatorFnDef.params
    • OperatorFnDef.forced_params
  • OperatorRandomDef
    • OperatorRandomDef.operator_fn
    • OperatorRandomDef.params
    • OperatorRandomDef.forced_params
  • ObtainStatisticDef
    • ObtainStatisticDef.operator_fn
    • ObtainStatisticDef.params
    • ObtainStatisticDef.forced_params
  • OperatorSwarmDef
    • OperatorSwarmDef.operator_fn
    • OperatorSwarmDef.params
    • OperatorSwarmDef.forced_params

Module contents

Pure function implementation of mutation operators

class ObtainStatisticDef(operator_fn, params=<factory>, forced_params=<factory>)

Bases: object

Wrap a statistic‑computing function into an Operator.

This adapter is used for functions that compute a single summary vector (e.g., population mean, median, standard deviation) and store it as the new genotype (usually a single-row population).

Parameters:

operator_fncallable

Function with signature (population_matrix, rng, **kwargs) -> np.ndarray.

paramsdict, optional

Default keyword arguments.

forced_paramsdict, optional

Keyword arguments that override user-supplied ones.

Parameters:

• operator_fn (callable)

• params (dict)

• forced_params (dict)

Methods

__call__(population[, rng])

Compute a statistic and replace the population’s genotype.

operator_fn: callable

params: dict

forced_params: dict

class OperatorRandomDef(operator_fn, params=<factory>, forced_params=<factory>)

Bases: object

Bridge a random-style operator function into an Operator.

This wrapper is intended for operators that replace the genotype with entirely new random values (e.g., uniform sampling, initializer-based reset). It passes the population’s genotype matrix, the initializer, and the random state to the underlying function.

Parameters:

operator_fncallable

Function with signature (population_matrix, initializer, rng, **kwargs) -> np.ndarray.

paramsdict, optional

Default keyword arguments.

forced_paramsdict, optional

Keyword arguments that override any user-supplied ones.

Parameters:

• operator_fn (callable)

• params (dict)

• forced_params (dict)

Methods

__call__(population, initializer[, rng])

Execute the random operator and return a new population.

operator_fn: callable

params: dict

forced_params: dict

class OperatorSwarmDef(operator_fn, params=<factory>, forced_params=<factory>)

Bases: object

Bridge a swarm operator function into an Operator.

This wrapper is designed for operators that directly receive the whole Population object and the initializer, and are responsible for returning an updated Population themselves (e.g., PSO operators that need access to historical bests).

Parameters:

operator_fncallable

Function with signature (population, initializer, rng, **kwargs) -> Population.

paramsdict, optional

Default keyword arguments.

forced_paramsdict, optional

Keyword arguments that override user-supplied ones.

Parameters:

• operator_fn (callable)

• params (dict)

• forced_params (dict)

Methods

__call__(population, rng, **kwargs)

Execute the swarm operator and return the new population.

operator_fn: callable

params: dict

forced_params: dict

class OperatorFnDef(operator_fn, params=<factory>, forced_params=<factory>)

Bases: object

Bridge a matrix-to-matrix operator function into an Operator.

This wrapper accepts a callable that operates on a genotype matrix, fitness array, and random state, and turns it into an object that can be used directly on a Population. It merges user-supplied keyword arguments with stored defaults and forced parameters, then invokes the underlying function and updates the population’s genotype.

Parameters:

operator_fncallable

Function with signature (population_matrix, fitness_array, rng, **kwargs) -> np.ndarray.

paramsdict, optional

Default keyword arguments for the operator.

forced_paramsdict, optional

Keyword arguments that always override user-supplied ones.

Parameters:

• operator_fn (callable)

• params (dict)

• forced_params (dict)

Methods

__call__(population[, rng])

Execute the wrapped operator and return a new population.

operator_fn: callable

params: dict

forced_params: dict