BMPGA: A Bi-Objective Multi-population Genetic Algorithm for Multi-modal Function Optimization
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Bibliographic record
Abstract
This paper introduces two innovations into the world of multi-modal function optimization: a new multi-population genetic algorithm (GA), with two complementary fitness terms (called BMPGA); and a new similarity function that is used to decide whether two points belong to the same cluster or not, called recursive middling (RM). An empirical comparative study is carried out to provide evidence that RM is a better measure of similarity than Ursem's hill-valley (or HV) function. Another comparative study compares the performance of BMGA with our own single-fitness-term multi-population GA (SMPGA), and with Ursem's multinational GA (MGA). The results show the clear superiority of RM and BMPGA over HV and MGA, respectively. The results also point to the potential of introducing a new aspect to the field of multi-modal optimization, where various complementary (as opposed to competitive) objectives are used to maintain diversity, so the GA can find all the optima of a given fitness surface
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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