An Efficient Pareto Set Identification Approach for Multi-Objective Optimization on Black-Box Functions
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Bibliographic record
Abstract
Both multiple objectives and computation-intensive black-box functions often exist simultaneously in engineering design problems. Few of existing multi-objective optimization approaches addresses problems with expensive black-box functions. In this paper, a new method called the Pareto set pursing (PSP) method is developed. By developing sampling guidance functions, this approach progressively provides a designer with a rich and evenly distributed Pareto optimal points. This work describes PSP in detail with analysis of its properties. From testing and design application, PSP demonstrates considerable efficiency, accuracy, and robustness. Theoretical proof of convergence of PSP is also given. It is believed that PSP has a great potential to be a practical tool for multi-objective optimization problems.
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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