Comparative strategies for knowledge migration in Multi Objective Optimization Problems
Why this work is in the frame
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Bibliographic record
Abstract
This paper provides a comparative study of the knowledge migration strategies in multi-objective optimization problems. It offers different migration strategies which are inspired by the game theory model. The idea behind incorporating this strategy is to increase diversity among the population, avoid premature convergence and escape from local optima. It also provides a meaningful migration in context to the population environment. Migration can be according to the individual choice, the decision of best individuals in the subpopulation or by negotiation among the population. The strategies used are of economics background which include the social factor which makes the individuals use their knowledge and decide the region of migration. This allows the population to explore new regions in the search space and increase diversity as the migrating individual carries its culture knowledge to other populations. The proposed algorithm is tested against CEC 2015 expensive benchmark problems. Results depict that it leads to better performance when migration is carried out by making the use of the proposed strategy.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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