Identifying and exploiting the scale of a search space in differential evolution
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
Optimisation in multimodal landscapes involves two distinct tasks: identifying promising regions and location of the (local) optimum within each region. Progress towards the second task can interfere with the first by providing a misleading estimate of a region's value. Thresheld convergence is a generally applicable “meta”-heuristic designed to control an algorithm's rate of convergence and hence which mode of search it is using at a given time. Previous applications of thresheld convergence in differential evolution (DE) have shown considerable promise, but the question of which threshold values to use for a given (unknown) function landscape remains open. This work explores the use of clustering-based method to infer the distances between local optima in order to set a series of decreasing thresholds in a multi-start DE algorithm. Results indicate that on those problems where normal DE converges, the proposed strategy can lead to sizable improvements.
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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 | 0.001 | 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