Surrogate Model Assisted Evolutionary Algorithms: Performance Bound and Incremental Gaussian Process Model Updates
Why this work is in the frame
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Bibliographic record
Abstract
The performance of surrogate model assisted algorithms for black-box optimization is impacted by two factors: algorithmic design choices on how to use the surrogate model, and the ability of the model to accurately represent the true objective function. In an effort to better understand the potential of surrogate model assisted algorithms, we propose to decouple those factors by studying the performance of the algorithms assuming perfect models. As a result, we obtain a natural performance bound that algorithms that use real models can be compared against, and that also provides an indication of the goodness of those models. We employ that performance bound in order to analytically evaluate a surrogate model assisted (1 + 1)-ES. Using the same bound, we also investigate the potential of performing incremental updates of Gaussian process surrogate models in an attempt to reduce algorithm internal computational costs and find that significant savings can be achieved at the cost of a small deterioration of model accuracy.
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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 | 0.002 |
| 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