Machine Learning for Determining the Transition Point in Hybrid Metaheuristics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
High-level relay hybrids are among the most effective metaheuristics in multiple domains. However, the relay aspect of hybridization raises the problem of when to perform the transition from one algorithm to the next. This problem becomes more relevant in exploration-only exploitation-only hybrids, where each algorithm specializes in a specific task and performs rather poorly in the other. This paper presents a novel way of approaching the transition problem as a classification problem. Different classifiers are trained and tested on the MPS-CMAES hybrid; computational results are presented for the CEC'13 benchmark. The performance of the machine learning based hybrid confirms the effectiveness of the approach by achieving a significant improvement over the original hybrid.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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