metaheuristic_designer.strategies.classic.ES

class ES(initializer, mutation_op, crossover_op=None, parent_sel=None, survivor_sel=None, offspring_size=None, name='ES', rng=None, **kwargs)

Evolution Strategy (μ+λ or μ,λ).

Applies mutation (and optionally crossover) to the selected parents, then selects survivors. By default, no parent selection is performed (all individuals are used).

Parameters:

initializerInitializer

Population initializer.

mutation_opOperator

Mutation operator.

crossover_opOperator, optional

Crossover operator. If None, only mutation is applied.

parent_selParentSelection, optional

Parent selection (default: use the whole population).

survivor_selSurvivorSelection, optional

Survivor selection (default: generational).

offspring_sizeint, optional

Number of offspring per generation.

namestr, optional

Display name (default "ES").

**kwargs

Forwarded to VariablePopulation.

Attributes:

initializer

params

Access parameter values by attribute-style lookup.

population_size

Gets the amount of individuals in the population.

Parameters:

• initializer (Initializer)

• mutation_op (Operator)

• crossover_op (Optional[Operator])

• parent_sel (Optional[ParentSelection])

• survivor_sel (Optional[SurvivorSelection])

• offspring_size (Optional[int])

• name (str)

• rng (Optional[RNGLike])

Methods

reset

__init__(initializer, mutation_op, crossover_op=None, parent_sel=None, survivor_sel=None, offspring_size=None, name='ES', rng=None, **kwargs)

Parameters:

• initializer (Initializer)

• mutation_op (Operator)

• crossover_op (Operator | None)

• parent_sel (ParentSelection | None)

• survivor_sel (SurvivorSelection | None)

• offspring_size (int | None)

• name (str)

• rng (int | Generator | None)

Methods

__init__(initializer, mutation_op[, ...])
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)	Initializes the optimization search strategy.
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.