Clustering Center-based 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
In recent years, center-based sampling has demonstrated impressive results to enhance the efficiency and effectiveness of meta-heuristic algorithms. The strategy of the center-based sampling can be utilized at either or both the operation and/or population level. Despite the overall efficiency of the center-based sampling in population-based algorithms, utilization at operation level requires customizing the strategy for a specific algorithm which degrades the scheme's generalization. In this paper, we have proposed a population-level center-based sampling method which is operation independent and correspondingly can be embedded in any population-based optimization algorithm. In this study, we applied the proposed scheme for Differential Evolution (DE) algorithm to enhance the exploration and exploitation capabilities of the algorithm. We cluster candidate solutions and inject the centroid-based samples into the population to increase the overall quality of the population and thus decrease the risk of premature convergence and stagnation. By a high chance, the center-based samples are effectively generated in the promising regions of the search space. The proposed method has been benchmarked by employing CEC-2017 benchmark test suite on dimensions 30, 50, and 100. The results clearly indicate the superiority of the proposed scheme, and a detailed results analysis is provided.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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