Convergence analysis of the tabu-based real-coded small-world optimization algorithm
Why this work is in the frame
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Bibliographic record
Abstract
A novel, tabu-based real-coded small-world optimization algorithm (TR-SWA) is proposed. Tabu search is adopted to avoid duplicate searches of the real-coded small-world optimization algorithm (R-SWA). A crossover operator is introduced to construct search operators. The convergence behaviour of this TR-SWA scheme is shown by establishing the Markov model, and it is proved that TR-SWA meets the convergence theorem of a general random search algorithm proposed by Solis and Wets. Simultaneously, martingale convergence theorems are used to prove the nearly universal strong convergence of TR-SWA. Finally, five benchmark functions are introduced to evaluate the performance of TR-SWA: comparisons are made between TR-SWA, particle swarm optimization, binary-coded small-world optimization algorithm and R-SWA. Numerical experiments demonstrate that the addition of the tabu search improves the performance of R-SWA for most of the investigated optimization problems, and the global convergence of TR-SWA is guaranteed if the feasible set is bounded.
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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 | 0.001 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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