Differential evolution with center-based mutation for large-scale optimization
Why this work is in the frame
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Bibliographic record
Abstract
The Differential Evolution (DE) is proved to be a successful approach for solving challenging optimization problems. However, its performance is degraded when solving high dimensional problems. Several significant enhancements of the DE have been proposed in recent years, including a variant of it with modified main operators (i.e., mutation and crossover). This paper proposes a center-based mutation scheme, which is based on the utilization of center of the gravity as a base vector. This mutation scheme aims to generate the candidate solution by using the center of three randomly selected candidate solutions. This new scheme is evaluated on CEC 2013 LSGO benchmark functions on the dimension 1000, as well as on fifteen shifted discrete benchmark functions on dimensions 500 and 1000. Experimental results confirm that the new scheme achieves a great success rate in comparison with the classical DE over the most of the test problems in terms of convergence rate and solution accuracy.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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