saealib.CrossoverIntegerSBX#

class saealib.CrossoverIntegerSBX(prob, eta, *, prob_var=0.5)[ソース]#

ベースクラス: Crossover

Simulated Binary Crossover (SBX) for integer-valued dimensions.

Applies SBX then rounds offspring to the nearest integer.

パラメータ:

  • prob (float)

  • eta (float)

  • prob_var (float)

prob#

Individual-level crossover probability.

Type:

float

eta#

Distribution index. Larger values produce offspring closer to parents.

Type:

float

prob_var#

Per-variable crossover probability. Default is 0.5.

Type:

float

参照

[11]: Deb, K., & Agrawal, R. B. (1995). Simulated binary crossover for continuous search space. Complex Systems, 9(2), 115-148.

Methods

__init__

Initialize integer SBX crossover operator.

contract

Return the integer crossover contract.

crossover

Execute crossover for one parent group.

crossover_batch

Execute integer SBX 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

CrossoverIntegerSBX.__init__(prob, eta, *, prob_var=0.5)[ソース]#

Initialize integer SBX crossover operator.

パラメータ:

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

  • eta (float) -- Distribution index.

  • prob_var (float, optional) -- Per-variable crossover probability, by default 0.5.

CrossoverIntegerSBX.contract()[ソース]#

Return the integer crossover contract.

戻り値の型:

ComponentContract

CrossoverIntegerSBX.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

CrossoverIntegerSBX.crossover_batch(parents_batch, bounds=None, rng=Generator(PCG64) at 0x7FE0F47AA260)[ソース]#

Execute integer SBX crossover on a batch of parent pairs at once.

This is the primary integer SBX implementation, vectorized over the leading n_pair axis.

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) -- Lower and upper bounds. When provided and finite, the bounded SBX variant is applied before rounding.

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

戻り値:

Offspring individuals with integer values. shape = (n_pair, 2, dim)

戻り値の型:

np.ndarray

メモ

For n_pair > 1, a loop of separate single-pair crossover_batch calls (equivalently, separate crossover() calls) does not reproduce the same per-row results as one batched call with the same seeded rng. Drawing each random phase for the whole batch has a different draw order from interleaving those phases one pair at a time.

CrossoverIntegerSBX.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

CrossoverIntegerSBX.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.

CrossoverIntegerSBX.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