Bibliographic record
Abstract
The S-metaheuristic algorithms work with a single candidate-solution during the search process. That is why they are prone to be trapped in local optima. Many research has being conducted to speed up and also minimize their premature convergence. The Center-point Sampling was introduced by Rahnamayan and Wang in 2008. Based on their experiments, it has shown increase in probability of closeness of the unique point in the center of the search space, to an unknown solution, as the dimensionality of the problem increases. It means, the center is an exceptional point to be used as initial point, specially during solving large-scale black-box problems. In this paper, we investigate this phenomena on Simulated Annealing (SA). The purpose is to accelerate the convergence speed of the algorithm by using the center point as an initial point for SA algorithm. This modified version, called Center-Point-Based SA (CSA), is a very simple and effective idea to enhance SA. The experimental verifications are provided on seven shifted large-scale (i.e., D=300) benchmark functions to show improvements achieved by the CSA algorithm. Using the shifted version of the functions ensures there is no bias towards the center, and so towards CSA algorithm. The results confirm that CSA outperforms parent SA algorithm in overall.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".