metaheuristic_designer.strategies.EDA package

Submodules

metaheuristic_designer.strategies.EDA.PBIL module

Population-Based Incremental Learning (PBIL) strategies.

class BernoulliPBIL(initializer: Initializer, parent_sel: ParentSelection | None = None, survivor_sel: SurvivorSelection | None = None, name: str = 'BernoulliPBIL', offspring_size: int | SchedulableParameter | None = None, random_state=None, p=None, lr=0.001, noise=0, **kwargs)

Bases: VariablePopulation

PBIL for binary vectors using a Bernoulli distribution.

The probability vector p is updated each generation with a learning rate and optional Gaussian noise, then a new population is sampled.

Reference: https://doi.org/10.1016/j.swevo.2011.08.003

Parameters:

• initializer (Initializer) – Population initializer.

• parent_sel (ParentSelection, optional) – Parent selection method.

• survivor_sel (SurvivorSelection, optional) – Survivor selection method.

• name (str, optional) – Display name (default "BernoulliPBIL").

• offspring_size (int or SchedulableParameter, optional) – Number of offspring per generation.

• random_state (RNGLike, optional) – Random number generator.

• p (array-like, optional) – Initial probability vector. Defaults to uniform over [0,1].

• lr (float, optional) – Learning rate for updating p (default 1e-3).

• noise (float, optional) – Standard deviation of Gaussian noise added to p (default 0).

• **kwargs – Forwarded to VariablePopulation.

perturb(parents, **kwargs)

Applies operators to the population to get the next generation of individuals.

Parameters:

parents (Population) – The current parents that will be used in the search strategy.

Returns:

offspring – The list of individuals modified by the operators of the search strategy.

Return type:

Population

class BinomialPBIL(initializer: Initializer, parent_sel: ParentSelection | None = None, survivor_sel: SurvivorSelection | None = None, name: str = 'BernoulliPBIL', offspring_size: int | SchedulableParameter | None = None, random_state=None, p=0.5, n=None, lr=0.001, noise=0, **kwargs)

Bases: VariablePopulation

PBIL for discrete vectors using a Binomial distribution.

Reference: https://doi.org/10.1016/j.swevo.2011.08.003

Parameters:

• initializer (Initializer) – Population initializer.

• parent_sel (ParentSelection, optional) – Parent selection method.

• survivor_sel (SurvivorSelection, optional) – Survivor selection method.

• name (str, optional) – Display name (default "BinomialPBIL").

• offspring_size (int or SchedulableParameter, optional) – Number of offspring per generation.

• random_state (RNGLike, optional) – Random number generator.

• p (float or array-like, optional) – Initial success probability (default 0.5).

• n (int or array-like) – Number of trials. Must be provided; there is no default.

• lr (float, optional) – Learning rate (default 1e-3).

• noise (float, optional) – Gaussian noise standard deviation (default 0).

• **kwargs – Forwarded to VariablePopulation.

perturb(parents, **kwargs)

Applies operators to the population to get the next generation of individuals.

Parameters:

parents (Population) – The current parents that will be used in the search strategy.

Returns:

offspring – The list of individuals modified by the operators of the search strategy.

Return type:

Population

class GaussianPBIL(initializer: Initializer, parent_sel: ParentSelection | None = None, survivor_sel: SurvivorSelection | None = None, name: str = 'GaussianPBIL', offspring_size: int | SchedulableParameter | None = None, random_state=None, loc=None, scale=1, lr=0.001, noise=0, **kwargs)

Bases: VariablePopulation

PBIL for continuous vectors using a Gaussian distribution.

The location vector loc is updated each generation with a learning rate and optional Gaussian noise, then a new population is sampled.

Reference: https://doi.org/10.1016/j.swevo.2011.08.003

Parameters:

• initializer (Initializer) – Population initializer.

• parent_sel (ParentSelection, optional) – Parent selection method.

• survivor_sel (SurvivorSelection, optional) – Survivor selection method.

• name (str, optional) – Display name (default "GaussianPBIL").

• offspring_size (int or SchedulableParameter, optional) – Number of offspring per generation.

• random_state (RNGLike, optional) – Random number generator.

• loc (array-like, optional) – Initial mean vector (default None; the operator uses a fallback).

• scale (float or array-like, optional) – Standard deviation (default 1).

• lr (float, optional) – Learning rate (default 1e-3).

• noise (float, optional) – Gaussian noise standard deviation added to loc (default 0).

• **kwargs – Forwarded to VariablePopulation.

perturb(parents, **kwargs)

Applies operators to the population to get the next generation of individuals.

Parameters:

parents (Population) – The current parents that will be used in the search strategy.

Returns:

offspring – The list of individuals modified by the operators of the search strategy.

Return type:

Population

metaheuristic_designer.strategies.EDA.UMDA module

Univariate Marginal Distribution Algorithm (UMDA) strategies.

class BernoulliUMDA(initializer: Initializer, parent_sel: ParentSelection | None = None, survivor_sel: SurvivorSelection | None = None, name: str = 'BernoulliUMDA', offspring_size: int | SchedulableParameter | None = None, random_state=None, p: number | float | int | SchedulableParameter = 0.5, noise: number | float | int | SchedulableParameter = 0, **kwargs)

Bases: VariablePopulation

UMDA for binary vectors using a Bernoulli distribution.

The probability vector is estimated from the selected parents (no smoothing). Gaussian noise can optionally be added.

Reference: https://doi.org/10.1016/j.swevo.2011.08.003

Parameters:

• initializer (Initializer) – Population initializer.

• parent_sel (ParentSelection, optional) – Parent selection method.

• survivor_sel (SurvivorSelection, optional) – Survivor selection method.

• name (str, optional) – Display name (default "BernoulliUMDA").

• offspring_size (int or SchedulableParameter, optional) – Number of offspring per generation.

• random_state (RNGLike, optional) – Random number generator.

• p (float or array-like, optional) – Initial probability (default 0.5).

• noise (float, optional) – Gaussian noise standard deviation (default 0).

• **kwargs – Forwarded to VariablePopulation.

perturb(parents, **kwargs)

Applies operators to the population to get the next generation of individuals.

Parameters:

parents (Population) – The current parents that will be used in the search strategy.

Returns:

offspring – The list of individuals modified by the operators of the search strategy.

Return type:

Population

class BinomialUMDA(initializer: Initializer, parent_sel: ParentSelection | None = None, survivor_sel: SurvivorSelection | None = None, name: str = 'BinomialUMDA', offspring_size: int | SchedulableParameter | None = None, random_state: int | Generator | None = None, p: number | float | int | SchedulableParameter = 0.5, n: number | float | int | SchedulableParameter | None = None, noise=0, **kwargs)

Bases: VariablePopulation

UMDA for discrete vectors using a Binomial distribution.

Reference: https://doi.org/10.1016/j.swevo.2011.08.003

Parameters:

• initializer (Initializer) – Population initializer.

• parent_sel (ParentSelection, optional) – Parent selection method.

• survivor_sel (SurvivorSelection, optional) – Survivor selection method.

• name (str, optional) – Display name (default "BinomialUMDA").

• offspring_size (int or SchedulableParameter, optional) – Number of offspring per generation.

• random_state (RNGLike, optional) – Random number generator.

• p (float or array-like, optional) – Initial success probability (default 0.5).

• n (int or array-like) – Number of trials. Must be provided; there is no default.

• noise (float, optional) – Gaussian noise standard deviation (default 0).

• **kwargs – Forwarded to VariablePopulation.

perturb(parents, **kwargs)

Applies operators to the population to get the next generation of individuals.

Parameters:

parents (Population) – The current parents that will be used in the search strategy.

Returns:

offspring – The list of individuals modified by the operators of the search strategy.

Return type:

Population

class GaussianUMDA(initializer: Initializer, parent_sel: ParentSelection | None = None, survivor_sel: SurvivorSelection | None = None, name: str = 'GaussianUMDA', offspring_size: int | SchedulableParameter | None = None, random_state=None, loc: number | float | int | ndarray[tuple[int], floating] | ndarray[tuple[int], integer] | ndarray[tuple[int], uint8 | bool] | SchedulableParameter = 0, scale: number | float | int | ndarray[tuple[int], floating] | ndarray[tuple[int], integer] | ndarray[tuple[int], uint8 | bool] | SchedulableParameter = 1, noise: number | float | int | SchedulableParameter = 0, **kwargs)

Bases: VariablePopulation

UMDA for continuous vectors using a Gaussian distribution.

The location vector is estimated from the selected parents. Gaussian noise can optionally be added.

Reference: https://doi.org/10.1016/j.swevo.2011.08.003

Parameters:

• initializer (Initializer) – Population initializer.

• parent_sel (ParentSelection, optional) – Parent selection method.

• survivor_sel (SurvivorSelection, optional) – Survivor selection method.

• name (str, optional) – Display name (default "GaussianUMDA").

• offspring_size (int or SchedulableParameter, optional) – Number of offspring per generation.

• random_state (RNGLike, optional) – Random number generator.

• loc (float or array-like, optional) – Initial mean (default 0).

• scale (float or array-like, optional) – Standard deviation (default 1).

• noise (float, optional) – Gaussian noise standard deviation added to loc (default 0).

• **kwargs – Forwarded to VariablePopulation.

perturb(parents, **kwargs)

Applies operators to the population to get the next generation of individuals.

Parameters:

parents (Population) – The current parents that will be used in the search strategy.

Returns:

offspring – The list of individuals modified by the operators of the search strategy.

Return type:

Population

metaheuristic_designer.strategies.EDA.cross_entropy_method module

Cross-Entropy Method (CEM) strategy.

class CrossEntropyMethod(initializer: Initializer, name: str = 'CrossEntropyMethod', random_state: int | Generator | None = None, elite_amount: int | SchedulableParameter | None = None, scale: ndarray[tuple[int], floating] | ndarray[tuple[int], integer] | ndarray[tuple[int], uint8 | bool] | str = 'calculated', **kwargs)

Bases: StaticPopulation

Cross-Entropy Method for continuous optimisation.

At each generation, the best individuals are selected and the mean of a Gaussian distribution is updated to their location, optionally with a scale estimated from the data. New solutions are sampled from this distribution.

Note

This class will be refactored when the EDA interface is finalised. Smoothing (learning rate) for the mean still needs to be added.

Parameters:

• initializer (Initializer) – Population initializer.

• name (str, optional) – Display name (default "CrossEntropyMethod").

• random_state (RNGLike, optional) – Random number generator.

• elite_amount (int or SchedulableParameter, optional) – Number of best individuals used to estimate the distribution.

• scale (VectorLike or "calculated", optional) – Standard deviation of the Gaussian. If "calculated", it is estimated from the selected individuals.

• **kwargs – Forwarded to StaticPopulation.

Module contents

Estimation of Distribution Algorithms (EDA) strategies.

class BernoulliPBIL(initializer: Initializer, parent_sel: ParentSelection | None = None, survivor_sel: SurvivorSelection | None = None, name: str = 'BernoulliPBIL', offspring_size: int | SchedulableParameter | None = None, random_state=None, p=None, lr=0.001, noise=0, **kwargs)

Bases: VariablePopulation

PBIL for binary vectors using a Bernoulli distribution.

The probability vector p is updated each generation with a learning rate and optional Gaussian noise, then a new population is sampled.

Reference: https://doi.org/10.1016/j.swevo.2011.08.003

Parameters:

• initializer (Initializer) – Population initializer.

• parent_sel (ParentSelection, optional) – Parent selection method.

• survivor_sel (SurvivorSelection, optional) – Survivor selection method.

• name (str, optional) – Display name (default "BernoulliPBIL").

• offspring_size (int or SchedulableParameter, optional) – Number of offspring per generation.

• random_state (RNGLike, optional) – Random number generator.

• p (array-like, optional) – Initial probability vector. Defaults to uniform over [0,1].

• lr (float, optional) – Learning rate for updating p (default 1e-3).

• noise (float, optional) – Standard deviation of Gaussian noise added to p (default 0).

• **kwargs – Forwarded to VariablePopulation.

perturb(parents, **kwargs)

Applies operators to the population to get the next generation of individuals.

Parameters:

parents (Population) – The current parents that will be used in the search strategy.

Returns:

offspring – The list of individuals modified by the operators of the search strategy.

Return type:

Population

class BernoulliUMDA(initializer: Initializer, parent_sel: ParentSelection | None = None, survivor_sel: SurvivorSelection | None = None, name: str = 'BernoulliUMDA', offspring_size: int | SchedulableParameter | None = None, random_state=None, p: number | float | int | SchedulableParameter = 0.5, noise: number | float | int | SchedulableParameter = 0, **kwargs)

Bases: VariablePopulation

UMDA for binary vectors using a Bernoulli distribution.

The probability vector is estimated from the selected parents (no smoothing). Gaussian noise can optionally be added.

Reference: https://doi.org/10.1016/j.swevo.2011.08.003

Parameters:

• initializer (Initializer) – Population initializer.

• parent_sel (ParentSelection, optional) – Parent selection method.

• survivor_sel (SurvivorSelection, optional) – Survivor selection method.

• name (str, optional) – Display name (default "BernoulliUMDA").

• offspring_size (int or SchedulableParameter, optional) – Number of offspring per generation.

• random_state (RNGLike, optional) – Random number generator.

• p (float or array-like, optional) – Initial probability (default 0.5).

• noise (float, optional) – Gaussian noise standard deviation (default 0).

• **kwargs – Forwarded to VariablePopulation.

perturb(parents, **kwargs)

Applies operators to the population to get the next generation of individuals.

Parameters:

parents (Population) – The current parents that will be used in the search strategy.

Returns:

offspring – The list of individuals modified by the operators of the search strategy.

Return type:

Population

class BinomialPBIL(initializer: Initializer, parent_sel: ParentSelection | None = None, survivor_sel: SurvivorSelection | None = None, name: str = 'BernoulliPBIL', offspring_size: int | SchedulableParameter | None = None, random_state=None, p=0.5, n=None, lr=0.001, noise=0, **kwargs)

Bases: VariablePopulation

PBIL for discrete vectors using a Binomial distribution.

Reference: https://doi.org/10.1016/j.swevo.2011.08.003

Parameters:

• initializer (Initializer) – Population initializer.

• parent_sel (ParentSelection, optional) – Parent selection method.

• survivor_sel (SurvivorSelection, optional) – Survivor selection method.

• name (str, optional) – Display name (default "BinomialPBIL").

• offspring_size (int or SchedulableParameter, optional) – Number of offspring per generation.

• random_state (RNGLike, optional) – Random number generator.

• p (float or array-like, optional) – Initial success probability (default 0.5).

• n (int or array-like) – Number of trials. Must be provided; there is no default.

• lr (float, optional) – Learning rate (default 1e-3).

• noise (float, optional) – Gaussian noise standard deviation (default 0).

• **kwargs – Forwarded to VariablePopulation.

perturb(parents, **kwargs)

Applies operators to the population to get the next generation of individuals.

Parameters:

parents (Population) – The current parents that will be used in the search strategy.

Returns:

offspring – The list of individuals modified by the operators of the search strategy.

Return type:

Population

class BinomialUMDA(initializer: Initializer, parent_sel: ParentSelection | None = None, survivor_sel: SurvivorSelection | None = None, name: str = 'BinomialUMDA', offspring_size: int | SchedulableParameter | None = None, random_state: int | Generator | None = None, p: number | float | int | SchedulableParameter = 0.5, n: number | float | int | SchedulableParameter | None = None, noise=0, **kwargs)

Bases: VariablePopulation

UMDA for discrete vectors using a Binomial distribution.

Reference: https://doi.org/10.1016/j.swevo.2011.08.003

Parameters:

• initializer (Initializer) – Population initializer.

• parent_sel (ParentSelection, optional) – Parent selection method.

• survivor_sel (SurvivorSelection, optional) – Survivor selection method.

• name (str, optional) – Display name (default "BinomialUMDA").

• offspring_size (int or SchedulableParameter, optional) – Number of offspring per generation.

• random_state (RNGLike, optional) – Random number generator.

• p (float or array-like, optional) – Initial success probability (default 0.5).

• n (int or array-like) – Number of trials. Must be provided; there is no default.

• noise (float, optional) – Gaussian noise standard deviation (default 0).

• **kwargs – Forwarded to VariablePopulation.

perturb(parents, **kwargs)

Applies operators to the population to get the next generation of individuals.

Parameters:

parents (Population) – The current parents that will be used in the search strategy.

Returns:

offspring – The list of individuals modified by the operators of the search strategy.

Return type:

Population

class CrossEntropyMethod(initializer: Initializer, name: str = 'CrossEntropyMethod', random_state: int | Generator | None = None, elite_amount: int | SchedulableParameter | None = None, scale: ndarray[tuple[int], floating] | ndarray[tuple[int], integer] | ndarray[tuple[int], uint8 | bool] | str = 'calculated', **kwargs)

Bases: StaticPopulation

Cross-Entropy Method for continuous optimisation.

At each generation, the best individuals are selected and the mean of a Gaussian distribution is updated to their location, optionally with a scale estimated from the data. New solutions are sampled from this distribution.

Note

This class will be refactored when the EDA interface is finalised. Smoothing (learning rate) for the mean still needs to be added.

Parameters:

• initializer (Initializer) – Population initializer.

• name (str, optional) – Display name (default "CrossEntropyMethod").

• random_state (RNGLike, optional) – Random number generator.

• elite_amount (int or SchedulableParameter, optional) – Number of best individuals used to estimate the distribution.

• scale (VectorLike or "calculated", optional) – Standard deviation of the Gaussian. If "calculated", it is estimated from the selected individuals.

• **kwargs – Forwarded to StaticPopulation.

class GaussianPBIL(initializer: Initializer, parent_sel: ParentSelection | None = None, survivor_sel: SurvivorSelection | None = None, name: str = 'GaussianPBIL', offspring_size: int | SchedulableParameter | None = None, random_state=None, loc=None, scale=1, lr=0.001, noise=0, **kwargs)

Bases: VariablePopulation

PBIL for continuous vectors using a Gaussian distribution.

The location vector loc is updated each generation with a learning rate and optional Gaussian noise, then a new population is sampled.

Reference: https://doi.org/10.1016/j.swevo.2011.08.003

Parameters:

• initializer (Initializer) – Population initializer.

• parent_sel (ParentSelection, optional) – Parent selection method.

• survivor_sel (SurvivorSelection, optional) – Survivor selection method.

• name (str, optional) – Display name (default "GaussianPBIL").

• offspring_size (int or SchedulableParameter, optional) – Number of offspring per generation.

• random_state (RNGLike, optional) – Random number generator.

• loc (array-like, optional) – Initial mean vector (default None; the operator uses a fallback).

• scale (float or array-like, optional) – Standard deviation (default 1).

• lr (float, optional) – Learning rate (default 1e-3).

• noise (float, optional) – Gaussian noise standard deviation added to loc (default 0).

• **kwargs – Forwarded to VariablePopulation.

perturb(parents, **kwargs)

Applies operators to the population to get the next generation of individuals.

Parameters:

parents (Population) – The current parents that will be used in the search strategy.

Returns:

offspring – The list of individuals modified by the operators of the search strategy.

Return type:

Population

class GaussianUMDA(initializer: Initializer, parent_sel: ParentSelection | None = None, survivor_sel: SurvivorSelection | None = None, name: str = 'GaussianUMDA', offspring_size: int | SchedulableParameter | None = None, random_state=None, loc: number | float | int | ndarray[tuple[int], floating] | ndarray[tuple[int], integer] | ndarray[tuple[int], uint8 | bool] | SchedulableParameter = 0, scale: number | float | int | ndarray[tuple[int], floating] | ndarray[tuple[int], integer] | ndarray[tuple[int], uint8 | bool] | SchedulableParameter = 1, noise: number | float | int | SchedulableParameter = 0, **kwargs)

Bases: VariablePopulation

UMDA for continuous vectors using a Gaussian distribution.

The location vector is estimated from the selected parents. Gaussian noise can optionally be added.

Reference: https://doi.org/10.1016/j.swevo.2011.08.003

Parameters:

• initializer (Initializer) – Population initializer.

• parent_sel (ParentSelection, optional) – Parent selection method.

• survivor_sel (SurvivorSelection, optional) – Survivor selection method.

• name (str, optional) – Display name (default "GaussianUMDA").

• offspring_size (int or SchedulableParameter, optional) – Number of offspring per generation.

• random_state (RNGLike, optional) – Random number generator.

• loc (float or array-like, optional) – Initial mean (default 0).

• scale (float or array-like, optional) – Standard deviation (default 1).

• noise (float, optional) – Gaussian noise standard deviation added to loc (default 0).

• **kwargs – Forwarded to VariablePopulation.

perturb(parents, **kwargs)

Applies operators to the population to get the next generation of individuals.

Parameters:

parents (Population) – The current parents that will be used in the search strategy.

Returns:

offspring – The list of individuals modified by the operators of the search strategy.

Return type:

Population