saealib.PymooAlgorithm

class saealib.PymooAlgorithm(pymoo_algorithm, *, allow_partial_tell=False)

ベースクラス: AskTellAlgorithm

Adapter wrapping a pymoo Algorithm (e.g. NSGA2()) as saealib's Algorithm.

Lets researchers who already have a pymoo algorithm reuse it unchanged inside saealib's ask-tell loop and surrogate-assisted strategies.

パラメータ:

• pymoo_algorithm (pymoo.core.algorithm.Algorithm) -- An already-constructed pymoo algorithm instance (not yet setup()).

• allow_partial_tell (bool, optional) -- PreSelectionStrategy truncates offspring before tell(), so tell() may receive only a subset of what ask() produced. By default this raises ConfigurationError, since index-coupled pymoo algorithms (e.g. differential evolution) would silently misalign parents and offspring under a subset. Set True to opt in and pass the subset through anyway. Default: False.

メモ

Engine mode. The wrapped pymoo algorithm owns its own population and internal state (survival mechanics, index-coupled operators, adaptive parameters). ctx.population is a mirror, refreshed from the pymoo algorithm's own .pop at the end of every tell(), not the source of truth. This is the only way to reuse a pymoo algorithm's own tested logic unchanged, but it comes with real limitations:

• No checkpoint/resume. OptimizationState.save() only serializes saealib's own arrays; the wrapped pymoo algorithm's internal state is not captured.

• ``n_offspring`` is ignored. The offspring count is fixed by the wrapped algorithm's own configuration (e.g. its pop_size); strategies that request a larger candidate pool for pre-selection cannot enlarge it here.

• Constrained problems combined with surrogate-only generations are unsupported. When a candidate never receives a true evaluation, saealib leaves its g/cv at schema defaults (0.0, looking falsely feasible) — this is an existing, library-wide limitation (SurrogatePrediction has no cv field), not something specific to this adapter, but it means PymooAlgorithm's constraint handling should not be trusted under SurrogateOnlyLoopStage/IB's untrue tail.

Initialization is handled by mirroring saealib's already-evaluated initial population into the pymoo algorithm's own initial ask() result before calling tell() on it, so the initial design-of- experiments is not duplicated or discarded.

A pymoo_idx column tracks each candidate's position in the pymoo infill population, so that tell() can recover the correct mapping even after SortByScoreStage/TopKSelectionStage reorder or truncate state.offspring between ask and tell.

Methods

__init__
ask	Generate offspring via the wrapped pymoo algorithm's own ask().
contract	Return the family contract with optional partial-feedback capability.
create_pareto_archive	Create a ParetoArchive with the correct direction for the problem.
get_required_attrs	Add a pymoo_idx column tracking position in the pymoo infill pop.
tell	Update the wrapped pymoo algorithm, then mirror its population into ctx.

Method Details

PymooAlgorithm.__init__(pymoo_algorithm, *, allow_partial_tell=False)

パラメータ:

• pymoo_algorithm (_PymooAlgorithmLike)

• allow_partial_tell (bool)

戻り値の型:

None

PymooAlgorithm.ask(request, state)

Generate offspring via the wrapped pymoo algorithm's own ask().

パラメータ:

• ctx (ExecutionContext) -- Current optimization context.

• provider (Dispatchable) -- Component provider.

• n_offspring (int or None, optional) -- Ignored; the offspring count is fixed by the wrapped pymoo algorithm's own configuration.

• request (ProposalRequest)

• state (StateView)

戻り値:

Candidates with x and pymoo_idx set.

戻り値の型:

Population

PymooAlgorithm.contract()

Return the family contract with optional partial-feedback capability.

戻り値の型:

ComponentContract

PymooAlgorithm.create_pareto_archive(attrs, init_capacity, problem)

Create a ParetoArchive with the correct direction for the problem.

パラメータ:

• attrs (list[PopulationAttribute])

• init_capacity (int)

• problem (Problem)

戻り値の型:

ParetoArchive

PymooAlgorithm.get_required_attrs(problem)

Add a pymoo_idx column tracking position in the pymoo infill pop.

パラメータ:

problem (Problem)

戻り値の型:

list[PopulationAttribute]

PymooAlgorithm.tell(feedback, state)

Update the wrapped pymoo algorithm, then mirror its population into ctx.

パラメータ:

• ctx (ExecutionContext) -- Current optimization context.

• provider (Dispatchable) -- Component provider.

• offspring (Population) -- Offspring population, possibly reordered or truncated relative to what ask() produced.

• feedback (FeedbackBatch)

• state (StateView)

戻り値の型:

StatePatch