saealib.CrossoverOnePoint

class saealib.CrossoverOnePoint(prob)

ベースクラス: Crossover

One-point crossover operator.

パラメータ:

prob (float)

prob

Individual-level crossover probability.

Type:

float

Methods

__init__	Initialize one-point crossover operator.
contract	Return the crossover contract.
crossover	Execute crossover for one parent group.
crossover_batch	Execute one-point 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

CrossoverOnePoint.__init__(prob)

Initialize one-point crossover operator.

パラメータ:

prob (float) -- Individual-level crossover probability.

CrossoverOnePoint.contract()

Return the crossover contract.

戻り値の型:

ComponentContract

CrossoverOnePoint.crossover(parent, bounds=None, rng=Generator(PCG64) at 0x7FE0F4773140)

Execute crossover for one parent group.

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

メモ

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().

パラメータ:

• 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()

戻り値:

Offspring individuals. shape = (n_offspring, dim)

戻り値の型:

np.ndarray

CrossoverOnePoint.crossover_batch(parents_batch, bounds=None, rng=Generator(PCG64) at 0x7FE0F47AA5E0)

Execute one-point crossover on a batch of parent pairs at once.

This is the primary one-point crossover implementation. Each pair gets its own independently drawn cut point, and offspring are reconstructed via a broadcast mask.

パラメータ:

• parents_batch (np.ndarray) -- Batch of parent pairs. shape = (n_pair, 2, dim)

• bounds (tuple of (np.ndarray, np.ndarray) or None) -- Ignored.

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

戻り値:

Offspring individuals. shape = (n_pair, 2, dim)

戻り値の型:

np.ndarray

メモ

rng.integers(1, dim, size=n_pair) consumes the same random stream as n_pair separate single-pair crossover_batch calls (equivalently, separate crossover() calls). Consequently, every row reproduces the corresponding result from that loop with an identically seeded rng.

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

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

パラメータ:

• 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.

戻り値:

Processed offspring. shape = (n_children, dim)

戻り値の型:

np.ndarray

CrossoverOnePoint.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.

パラメータ:

• 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.

戻り値:

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

戻り値の型:

np.ndarray

メモ

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.

CrossoverOnePoint.with_post(fn)

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

パラメータ:

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

戻り値:

Shallow copy with the hook registered.

戻り値の型:

Crossover