A Data-Centric Machine Learning Approach for Controlling Exploration in Estimation of Distribution Algorithms
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
Exploration plays a key role in the performance of metaheuristics. An algorithm should perform more exploration when reaching the "ideal search scale"; this happens when solutions are regularly sampled from different attraction basins. The moment this search scale is reached depends on the topological features of the objective function and the inherent randomness of the heuristic optimization process. Previous work on adjusting exploration have mostly used fixed rules based on fitness improvement, in this paper, we model it as a supervised machine learning problem. We apply a data-centric approach to understand whether variations in the data are more relevant than variations in the classification models. For our study we use the Estimation Multivariate Normal Algorithm with Thresheld Convergence, which provides an ideal framework as it allows us to directly control exploration through the γ parameter. Optimization results show that the machine learning hybrid significantly outperforms the baseline algorithm.
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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 | 0.003 | 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 | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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