saealib.Crossover

class saealib.Crossover

Bases: ABC

Base class for crossover operators.

n_parents

Number of parents required per crossover call. Default is 2. Subclasses may override this class attribute if they require a different number of parents.

Type:

int

n_children

Number of offspring produced per crossover call. Default is 2. Subclasses may override this class attribute if they produce a different number of offspring.

Type:

int

prob

Individual-level crossover probability.

Type:

float

Methods

__init__
contract	Return the crossover contract.
crossover	Execute crossover for one parent group.
crossover_batch	Execute crossover on a batch of parent pairs at once.
post_crossover	Post-crossover lifecycle hook; override to inject custom processing.
post_crossover_batch	Run the post-crossover lifecycle hook for a batch.
with_post	Return a copy of this operator with fn appended to the hook.

Method Details

Crossover.__init__()

Crossover.contract()

Return the crossover contract.

Return type:

ComponentContract

Crossover.crossover(parent, bounds=None, rng=Generator(PCG64) at 0x7F79179411C0)

Execute crossover for one parent group.

This method is derived from crossover_batch() by passing a single-pair batch.

Notes

Overriding only this method (not crossover_batch()) has no effect under GA’s default variation_execution="batch": the batch path calls crossover_batch() directly and never calls crossover(). Such an override only takes effect under variation_execution="sequential". To customize behavior under both modes, override crossover_batch().

Parameters:

• parent (np.ndarray) – Parent individuals. shape = (n_parents, dim)

• bounds (tuple of (np.ndarray, np.ndarray) or None) – Lower and upper bounds for each variable. None means unbounded.

• rng (np.random.Generator, optional) – Random number generator, by default np.random.default_rng()

Returns:

Offspring individuals. shape = (n_offspring, dim)

Return type:

np.ndarray

abstractmethod Crossover.crossover_batch(parents_batch, bounds=None, rng=Generator(PCG64) at 0x7F79179417E0)

Execute crossover on a batch of parent pairs at once.

This is the required primitive that every concrete crossover class must implement directly.

Parameters:

• parents_batch (np.ndarray) – Batch of parent groups. shape = (n_pair, n_parents, dim), i.e. a batch of what crossover() normally receives one instance of at a time (parents_batch[k] is what a single crossover(parent=parents_batch[k], ...) call would take as parent).

• bounds (tuple of (np.ndarray, np.ndarray) or None) – Lower and upper bounds for each variable. None means unbounded.

• rng (np.random.Generator, optional) – Random number generator, by default np.random.default_rng()

Returns:

Offspring individuals. shape = (n_pair, n_children, dim)

Return type:

np.ndarray

Notes

No prob gating is performed inside crossover_batch: the caller is responsible for having already filtered parents_batch down to only the pairs that should undergo crossover.

Crossover.post_crossover(offspring, parents, rng, ctx=None)

Post-crossover lifecycle hook; override to inject custom processing.

Parameters:

• offspring (np.ndarray) – Offspring produced by crossover. shape = (n_children, dim)

• parents (np.ndarray) – Parent individuals. shape = (n_parents, dim)

• rng (np.random.Generator) – Random number generator.

• ctx (ExecutionContext or None, optional) – Current optimization context.

Returns:

Processed offspring. shape = (n_children, dim)

Return type:

np.ndarray

Crossover.post_crossover_batch(offspring_batch, parents_batch, rng, ctx=None)

Run the post-crossover lifecycle hook for a batch.

The default implementation calls post_crossover() once per parent group, in order. Override this method to provide genuinely vectorized post-processing.

Parameters:

• offspring_batch (np.ndarray) – Offspring produced by crossover. shape = (n_pair, n_children, dim)

• parents_batch (np.ndarray) – Batch of parent groups. shape = (n_pair, n_parents, dim)

• rng (np.random.Generator) – Random number generator.

• ctx (ExecutionContext or None, optional) – Current optimization context.

Returns:

Processed offspring. shape = (n_pair, n_children, dim)

Return type:

np.ndarray

Notes

with_post() reassigns only the instance’s post_crossover() hook. A subclass override of this method is responsible for composing that hook itself. If the override does not call self.post_crossover, a hook installed with with_post() will not run in GA batch mode. It still runs in GA sequential mode, which calls post_crossover() directly.

Crossover.with_post(fn)

Return a copy of this operator with fn appended to the hook.

Parameters:

fn (callable) – fn(offspring, parents, rng, ctx) -> np.ndarray

Returns:

Shallow copy with the hook registered.

Return type:

Crossover