Metamodel-Based Optimization for Problems With Expensive Objective and Constraint Functions
Bibliographic record
Abstract
Current metamodel-based design optimization methods rarely deal with problems of not only expensive objective functions but also expensive constraints. In this work, we propose a novel metamodel-based optimization method, which aims directly at reducing the number of evaluations for both objective function and constraints. The proposed method builds on existing mode pursuing sampling method and incorporates two intriguing strategies: (1) generating more sample points in the neighborhood of the promising regions, and (2) biasing the generation of sample points toward feasible regions determined by the constraints. The former is attained by a discriminative sampling strategy, which systematically generates more sample points in the neighborhood of the promising regions while statistically covering the entire space, and the latter is fulfilled by utilizing the information adaptively obtained about the constraints. As verified through a number of test benchmarks and design problems, the above two coupled strategies result in significantly low number of objective function evaluations and constraint checks and demonstrate superior performance compared with similar methods in the literature. To the best of our knowledge, this is the first metamodel-based global optimization method, which directly aims at reducing the number of evaluations for both objective function and constraints.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".