Estimation distribution algorithms with differential mutation for multi-objective optimization problems
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Bibliographic record
Abstract
Estimation distribution algorithms (EDAs) have been widely used in single objective optimization problems. In this paper EDAs are combined with differential mutation (DM) to find Pareto optimal front for multi-objective optimization problems (MOPs). First, a modified extreme elitism selection method is used to choose some promising solutions as the parent solution. This selection represents some leading best solutions in the evolution to make EDAs form a primary evolutionary direction. Also, DM leads to a diversified population, which helps the algorithm to avoid premature convergence. A set of benchmark MOPs are used to test the proposed EDA/DM algorithm. The experimental results demonstrate that EDA/DM can achieve better performance on some MOPs than by several state-of-the-art multi-objective evolutionary algorithms.
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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 | 0.003 |
| 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