ribs.emitters.rankers.NSLCClassicRanker¶

class ribs.emitters.rankers.NSLCClassicRanker(seed: int | integer | None = None)[source]¶

Ranks solutions with the paper-faithful NSLC algorithm using genotypic diversity.

This ranker is a variant of NSLCRanker that automatically uses the genotypic_diversity field as the third objective in the non-dominated sort, implementing Lehman & Stanley’s original NSLC algorithm. This is the paper-faithful mode that uses three objectives:

Novelty (higher is better)
Local competition (higher is better)
Genotypic diversity (higher is better)

This ranker is intended for users who can provide a genotypic-diversity signal for their domain. If the genotypic_diversity field is not available from the archive or emitter, the ranker will raise a KeyError at ranking time.

For the practical 2-objective version that uses NSGA-II crowding distance instead, use NSLCRanker.

This ranker can only be used with archives that return both novelty and local_competition fields from their add method. Currently, this is ribs.archives.ProximityArchive.add() when the archive is constructed with local_competition=True.

Parameters:¶

seed: int | integer | None = None¶: Passed through to RankerBase; unused by this ranker but kept for API consistency with other rankers.

Methods

`rank`(emitter, archive, data, add_info)	Ranks solutions by fast non-dominated sort over (novelty, local_competition) -- plus an optional third diversity objective when `diversity_field` is set -- and breaks ties within each front with NSGA-II crowding distance.
`reset`(emitter, archive)	Resets the internal state of the ranker.

Attributes

`diversity_field`	Name of the optional third objective field, or `None`.

rank(emitter: EmitterBase, archive: ArchiveBase, data: dict[str, ndarray], add_info: dict[str, ndarray]) → tuple[ndarray, ndarray]¶

Ranks solutions by fast non-dominated sort over (novelty, local_competition) – plus an optional third diversity objective when diversity_field is set – and breaks ties within each front with NSGA-II crowding distance.

Parameters:¶

emitter: EmitterBase¶: Emitter that this ranker object belongs to.
archive: ArchiveBase¶: Archive used by emitter when creating and inserting solutions.
data: dict[str, ndarray]¶: Dict mapping from field names like solution and objective to arrays with data for the solutions.
add_info: dict[str, ndarray]¶: Information returned by an archive’s add() method.

Returns:¶

The first array (shape (batch_size,)) is an array of indices representing a ranking of the solutions and the second array (shape (batch_size,) or (batch_size, n_values)) is an array of values that this ranker used to rank the solutions. batch_size is the number of solutions and n_values is the number of values that the rank function used.

reset(emitter: EmitterBase, archive: ArchiveBase) → None¶

Resets the internal state of the ranker.

Parameters:¶

emitter: EmitterBase¶: Emitter that this ranker object belongs to.
archive: ArchiveBase¶: Archive used by emitter when creating and inserting solutions.

property diversity_field : str | None¶: Name of the optional third objective field, or None.