Constraint Importance Mode Pursuing Sampling for Continuous Global Optimization
Bibliographic record
Abstract
Many engineering design problems deal with global optimization of constrained black-box problems which is usually computation-intensive. Ref. [1] proposed a Mode-Pursuing Sampling (MPS) method for global optimization based on a sampling technique which systematically generates more sample points in the neighborhood of the function mode while statistically covering the entire problem domain. In this paper, we propose a novel and more efficient sampling technique which greatly enhances the performance of the MPS method, especially in the presence of expensive constraints. The effective sampling of the search space is attained via biasing the sample points towards feasible regions and being away from the forbidden regions. This is achieved by utilizing the incrementally obtained information about the constraints, hence, it is called Constraint-importance Mode Pursuing Sampling (CiMPS). According to intensive comparisons and experimental verifications, the new sampling technique is found to be more efficient in solving constrained optimization problems compared to the original MPS method. To the best of our knowledge, this is the first metamodel-based global optimization method that directly aims at reducing the number of function evaluations for both expensive objective functions and constraints.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".