saealib.CrossoverBLXAlpha#

class saealib.CrossoverBLXAlpha(prob, alpha)[source]#

Bases: Crossover

BLX-alpha crossover operator.

Parameters:

  • prob (float)

  • alpha (float)

prob#

Individual-level crossover probability.

Type:

float

alpha#

Alpha parameter for BLX-alpha crossover. Controls how far outside the parents’ range offspring can be generated.

Type:

float

Notes

Originates from Eshelman & Schaffer (1993); the primary paper has not been obtained and is credited here by name only. The interval [c_min - alpha*I, c_max + alpha*I] formula implemented in crossover_batch() has been verified against Herrera, Lozano & Verdegay (1998), which restates the BLX-alpha definition while surveying real-coded GA operators.

References

[40]: Herrera, F., Lozano, M., & Verdegay, J. L. (1998). Tackling real-coded genetic algorithms: Operators and tools for behavioural analysis. Artificial Intelligence Review, 12, 265-319.

Methods

__init__

Initialize BLX-alpha crossover operator.

contract

Return the crossover contract.

crossover

Execute crossover for one parent group.

crossover_batch

Execute BLX-alpha 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

CrossoverBLXAlpha.__init__(prob, alpha)[source]#

Initialize BLX-alpha crossover operator.

Parameters:

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

  • alpha (float) – Alpha parameter for BLX-alpha crossover.

CrossoverBLXAlpha.contract()#

Return the crossover contract.

Return type:

ComponentContract

CrossoverBLXAlpha.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

CrossoverBLXAlpha.crossover_batch(parents_batch, bounds=None, rng=Generator(PCG64) at 0x7F7917942DC0)[source]#

Execute BLX-alpha crossover on a batch of parent pairs at once.

This is the primary BLX-alpha implementation.

Parameters:

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

  • bounds (tuple of (np.ndarray, np.ndarray) or None) – Ignored; BLX-alpha is inherently unbounded.

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

Returns:

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

Return type:

np.ndarray

Notes

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 rng.uniform(size=(n_pair, dim)) for the whole batch has a different draw order from looping rng.uniform(size=dim).

CrossoverBLXAlpha.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

CrossoverBLXAlpha.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.

CrossoverBLXAlpha.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