Approximated unimodal region elimination-based global optimisation method for engineering design
Why this work is in the frame
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Bibliographic record
Abstract
Computer analysis and simulation-based design optimisation requires more computationally efficient global optimisation tools. In this work, a new global optimisation algorithm based on design experiments, region elimination and response surface modelling, namely, the Approximated Unimodal Region Elimination (AUMRE) method, is introduced. The approach divides the field of interest into several unimodal regions using design experiment data, identifies and ranks the regions that most likely contain the global minimum, forms a response surface model using additional design experiment data over the most promising region, identifies its minimum, removes this processed region and moves to the next most promising region. By avoiding redundant searches, the approach identifies the global optimum with a reduced number of objective function evaluations and computation effort. The new algorithm was tested using a variety of benchmark global optimisation problems and compared with several widely used global optimisation algorithms. The results present a comparable search accuracy and superior computation efficiency, making the new algorithm an ideal tool for computer analysis and simulation-based global design optimisation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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