metaheuristic_designer.operators.branch_operator module

Operator that randomly applies one operator from a list to each individual.

class BranchOperator(op_list, random_pick=True, name=None, encoding=None, rng=None, weights=None, p=None, **kwargs)

Bases: Operator

Operator that stochastically selects among several operators.

For each individual, one operator from op_list is chosen according to the configured method (random with given probability, or manually picked). This allows e.g. applying mutation with a certain probability while leaving the rest untouched.

Parameters:

op_listlist of Operator

The candidate operators.

random_pickbool, optional

Whether to pick an operator at random or by specifying an index (default True).

namestr, optional

Display name; defaults to "method(op_names)".

encodingEncoding, optional

Encoding applied to the genotype.

rngRNGLike, optional

Random number generator.

weights: VectorLike, optional

Weights of each operator when choosing at random.

pfloat, optional

Probability of selecting the first operator (default 0.5). Only applied when op_list has length 2 and no weights are specified.

**kwargs

Additional keyword arguments stored as schedulable parameters.

Attributes:

params

Access parameter values by attribute-style lookup.

Parameters:

• op_list (Iterable[Operator])

• random_pick (bool)

• name (str)

• encoding (Optional[Encoding])

• rng (Optional[RNGLike])

• weights (Optional[VectorLike])

• p (float)

Methods

__call__(population)	Shorthand for evolve().
choose_index(idx)	Manually chooses the operator to use next
evolve(population)	Apply a random operator to each individual according to the branch method.
gather_params()	Collect parameters from this operator and all sub-operators.
get_params()	Return a copy of the current parameter dictionary.
get_state()	Gets the current state of the algorithm as a dictionary.
store_kwargs([progress])	Store keyword arguments and evaluate them at the given progress.
update(progress)	Update schedulable parameters and propagate to sub-operators.
update_kwargs([progress])	Add or replace parameters and immediately evaluate them.

gather_params()

Collect parameters from this operator and all sub-operators.

Return type:

dict

Returns:

dict

Flat dictionary with dotted keys.

evolve(population)

Apply a random operator to each individual according to the branch method.

Return type:

Population

Parameters:

populationPopulation

The current population.

Returns:

Population

The modified population.

Parameters:

population (Population)

choose_index(idx)

Manually chooses the operator to use next

Parameters:

idxint

Index of the operator in the list.

Parameters:

idx (ndarray[tuple[int], floating] | ndarray[tuple[int], integer] | ndarray[tuple[int], uint8 | bool] | number | float | int)

update(progress)

Update schedulable parameters and propagate to sub-operators.

Parameters:

progressfloat

Current progress of the algorithm (0-1).

Parameters:

progress (float)

get_state()

Gets the current state of the algorithm as a dictionary.

Return type:

dict

Returns:

state: dict

The complete state of the operator.