Population Migration Using Dominance in Multi-population Cultural Algorithms
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Bibliographic record
Abstract
In this study we introduce a new method to enable the migration of individuals from one population to another using the concept of dominance in Multi-Population Cultural Algorithms (MPCA's). The MPCA's artificial population comprises of agents that belong to a certain sub-population. Multiple sub-populations are generated, each running its own Cultural Algorithm (CA). In this work we create a dominance-MPCA (D-MPCA) with a network of populations that implements a dominance strategy. We hypothesize that the evolutionary advantage of dominance can help improve the performance of MPCA in general optimization problems. The Sphere function from the CEC 2013 benchmark optimization functions is used to calculate the fitness value of the individuals. We observe how the populations adapt to the changes. Preliminary results show improved performance in our proposed D-MPCA over traditional MPCA.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 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