metaheuristic_designer.strategies.classic.CMA_ES

class CMA_ES(initializer, survivor_sel=None, name='CMA-ES', offspring_size=None, rng=None, mean=None, sigma=None, cond_tol=100000000.0, sigma_tol=1e-10, **kwargs)

Covariance Matrix Adaptation Evolution Strategy (CMA-ES).

This is a population-based algorithm that samples new solutions from a multivariate normal distribution whose mean and covariance are adapted each generation based on the best individuals.

Note

The architecture of this class is provisional. It currently overrides initialize() and perturb() directly. Once the distribution-based (EDA) abstraction is in place, CMA-ES will be rewritten to use that common interface.

Parameters:

initializerInitializer

Provides population size and genotype shape, but does not generate the initial solutions.

survivor_selSurvivorSelection, optional

How survivors are selected. Defaults to the strategy’s default (generational).

namestr, optional

Display name (default "CMA-ES").

offspring_sizeint or SchedulableParameter, optional

Number of offspring per generation. If None, the initializer’s population size is used.

rngRNGLike, optional

Random number generator.

meanVectorLike, optional

Initial mean vector. If not given, it is computed from the objective’s bounds (or randomly if no bounds exist).

sigmaVectorLike, optional

Initial step size. If not given, a default is computed.

**kwargs

Forwarded to VariablePopulation.

Attributes:

params

Access parameter values by attribute-style lookup.

population_size

Gets the amount of individuals in the population.

Parameters:

• initializer (Initializer)

• survivor_sel (SurvivorSelection)

• name (str)

• offspring_size (Optional[int | SchedulableParameter])

• mean (Optional[VectorLike])

• sigma (Optional[VectorLike])

• cond_tol (float)

• sigma_tol (float)

Methods

reset

__init__(initializer, survivor_sel=None, name='CMA-ES', offspring_size=None, rng=None, mean=None, sigma=None, cond_tol=100000000.0, sigma_tol=1e-10, **kwargs)

Parameters:

• initializer (Initializer)

• survivor_sel (SurvivorSelection | None)

• name (str)

• offspring_size (int | SchedulableParameter | None)

• mean (ndarray[tuple[int], floating] | ndarray[tuple[int], integer] | ndarray[tuple[int], uint8 | bool] | None)

• sigma (ndarray[tuple[int], floating] | ndarray[tuple[int], integer] | ndarray[tuple[int], uint8 | bool] | None)

• cond_tol (float)

• sigma_tol (float)

Methods

__init__(initializer[, survivor_sel, name, ...])
estimate_parameters(population)	Update the distribution parameters
extra_report()	Hook called at the end of the optimization (intended for subclasses).
extra_step_info()	Hook called after each generation (intended for subclasses).
gather_parameters()	Collect the current parameters from all sub-components.
get_params()	Return a copy of the current parameter dictionary.
get_state()	Gets the current state of the search strategy as a dictionary.
initialize(objfunc)	Create the initial population by sampling from the current distribution.
reset(objfunc)
step(prev_population, objfunc)	Performs a single iteration of the algorithm on a given population.
store_kwargs([progress])	Store keyword arguments and evaluate them at the given progress.
update(progress)	Advances the state of the search by one iteration.
update_kwargs([progress])	Add or replace parameters and immediately evaluate them.