The Effects of CMA-ES Style Selection and Restart Criteria on DE
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Bibliographic record
Abstract
Over the years, a lot of research has gone into the creation of different mutation operators and adaptive parameters for differential evolution (DE). However, the literature is fairly quiet about automatically setting population size and completely silent about varying the selection operator used within DE. In this paper, we steal a page from CMA-ES/IPOP: using ES-style µ+λ selection, which selects across the entire population, in place of more individualistic DE selection with its use of local selection on the target and its child. We find that the most effective choice of selection can depend on the function being optimized, although for most of the functions we tested, the original DE selection was preferable. When adding IPOP style restarting, EqualFunValHist is the most applicable of the stagnation criteria, and it is used to trigger the doubling of the population size upon restart. The initial population size is set to the same as CMA-ES. Here we find, that the restartable DE behave as well and better as regular DE with population size set as lower than the default settings used.
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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