EXHAUSTIVE SEARCH APPROXIMATIONS IN DESIGN OPTIMIZATION: AN ALGORITHMIC IMPLEMENTATION
Bibliographic record
Abstract
In this study, a new idealization to the exhaustive search optimization problem is given, Three formulations are given to a design-engineering problem using standard arrays: these are L243/ L27 OA, L27/ L108 OA and L81/ L108 OA and their sub-families. We found that certain idealizations, though realistic are still considered NP hard problems. As a remedy, a new exhaustive sequential algorithm is developed that solves NP hard problems in n sequential stages. Both the deterministic and statistical solutions are given and conclusions are drawn regarding their convergence properties. Composite arrays are developed out of the standard ones and simulations results indicate that certain composite arrays have better variance and convergence properties than other standard ones. These results are considered as part of the authors’ work to develop efficient statistical optimization techniques. Review of optimization related literatures indicates the strong need for development of global optimization techniques. A standard case study is used for simulation and comparison. As an extension, the same case study is reformulated based on minimum sensitivity, modeled using exhaustive search concepts and solved. Results indicate the potential of the new formulation to result in least sensitive solutions with low variances
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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 | 0.002 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".