Increasing Features in MAP-Elites Using an Age-Layered Population Structure
Bibliographic record
Abstract
The multi-dimensional archive of phenotypic elites (MAP-Elites) algorithm is a popular evolutionary algorithm which returns a highly diverse set of elite solutions. The population is separated into a grid-like feature space defined by user-specified feature dimensions where each cell of the grid corresponds to a unique behaviour combination. The algorithm is conceptually simple and effective at producing high-quality, diverse solutions, but it comes with a major limitation on its exploratory capabilities. With every added feature, the set of solutions grows exponentially, making high-dimensional feature spaces infeasible. This work proposes a way of increasing features with the novel Age-Layered MAP-Elites (ALME) algorithm where the population is separated into age-layers and each layer has its own feature space. By using different features in the layers, the population migrates up through the layers experiencing selective pressure towards different features. This algorithm is applied to a simulated intelligent agent environment where agents are controlled by genetic programming (GP) trees to observe interesting emergent behaviours in underexplored regions of the feature space. It is observed that ALME is capable of producing a high-quality and diverse set of solutions that is competitive with traditional MAP-Elites without the combinatorial explosion in the resulting number of solutions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".