Population-based coordinate descent algorithm with majority voting
Bibliographic record
Abstract
Many real-world optimization problems belong to the class of expensive problems and the costly process of computing fitness value or gradient of the objective function may cause the failure of various optimization algorithms to solve them quickly. Because of the low computation and memory requirements of Coordinate Descent (CD) search methods they are suitable algorithms to optimize these problems. Despite the efficiency of CD methods, searching a large-scale search space just by using one candidate solution decreases the exploration capability of the algorithm. In this paper, a novel population-based version of the CD algorithm called Population-Based Coordinate Descent (PBCD) is proposed which is an efficacious method for tackling such problems using the collective intelligence and collaboration of the population. It takes advantage of three phases of locating the region of interest, folding the search space, and communication among the population members with majority voting to find more promising regions in the search space. As it shrinks the search space swiftly, it needs a low computational budget for finding the optimal value per coordinate and ultimately in overall. To investigate its performance, we benchmarked it on CEC-2017 test suite consisting of 29 low-scale problems with dimensions of 30, 50, and 100.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".