Rethinking multilevel selection in genetic programming
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper aims to improve the capability of genetic programming to tackle the evolution of cooperation: evolving multiple partial solutions that collaboratively solve structurally and functionally complex problems. A multilevel genetic programming approach is presented based on a new computational multilevel selection framework [19]. This approach considers biological group selection theory to encourage cooperation, and a new cooperation operator to build solutions hierarchically. It extends evolution from individuals to multiple group levels, leading to good performance on both individuals and groups. The applicability of this approach is evaluated on 7 multi-class classification problems with different features, such as non-linearity, skewed data distribution and large feature space. The results, when compared to other cooperative evolutionary algorithms in the literature, demonstrate that this approach improves solution accuracy and consistency, and simplifies solution complexity. In addition, the problem is decomposed as a result of evolution without human interference.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it