Proposing effective coordinate search methods for solving large-scale expensive black-box optimization problems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In engineering and science, optimization plays a vital role in many real-world applications. In this work, several novel optimization algorithms based on Coordinate Search (CS) algorithm are proposed. CS is a gradient-free technique and we have enhanced them for solving Black-box, non-convex, and expensive large-scale problems. These CS-based algorithms can handle mixed-type variables. When an optimization problem is large-scale and expensive, it is a very challenging problem to solve because it is intersecting two conflicting properties. Large-scale problems require extensive fitness evaluations, but each evaluation is time consuming. It gets more challenging when the budget is limited, which is the case in most real-word applications. The proposed CS-based algorithms reduce the search space exponentially; this makes it a powerful method for optimizing high-dimensional problems with limited budget. The proposed algorithms show a very promising performance on optimizing high-dimensional problems; tested on the CEC-2013 benchmarks problems and neural network training.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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