Performance evaluation of the NGHS metaheuristic as an alternative to the dynamic adaptive GA in the CREASE tool in SAS profile analysis of nanoparticulate system
Bibliographic record
Abstract
This research focused on intervening in the optimization algorithm used by the Computational Reverse-Engineering Analysis for Scattering Experiments (CREASE) tool to analyze small-angle scattering (SAS) profiles using the Rigid-Body model. CREASE uses the genetic algorithm (GA) with dynamic adaptation as its optimization algorithm. The aim is to evaluate the performance of CREASE by replacing the GA with a Harmony Search (HS)-based metaheuristic, specifically the Nobel Global Harmony Search (NGHS), in the analysis of SAS profiles of low-concentration solutions vesicles-assembled amphiphilic macromolecules. Results showed that NGHS achieved similar accuracy to GA but with higher efficiency, achieving similar quality solutions with only one-sixth, and in some cases one-tenth, the number of fitness function evaluations used by GA. Besides, CREASE-NGHS achieved SAS profile analysis convergence with less than half the number of fitness function evaluations, saving computational resources and facilitating a more complete analysis. In addition, NGHS addressed some shortcomings of the GA optimization process and facilitated its use and adaptation to distinct types of samples for users with little experience in optimization.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".