Population-level center-based sampling for meta-heuristic algorithms
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 recent years, the challenge of enhancing the efficiency and effectiveness of meta-heuristic algorithms has gained significant attention. Center-based sampling has shown promise in addressing this challenge, yet its application often requires customization for specific algorithms, limiting its generalizability. This study identifies a gap in the literature regarding the operation-independent application of center-based sampling. To address this, we propose a novel center-based sampling strategy at the population level, which can be seamlessly integrated into any population-based optimization algorithm. Our approach employs a collaborative multi-parent method to generate multiple center-based solutions, thereby increasing diversity and exploiting the solution space more effectively. We introduce two specific strategies: cluster-driven center-based sampling for single-objective optimization and ranking-driven center-based sampling for multi-objective optimization. The performance of these strategies is evaluated using the benchmark functions for the CEC-2017 competition on 5 single- and 6 many-objective evolutionary algorithms, demonstrating 40 % ∼ 100 % statistical fitness improvement ratio over parent meta-heuristic algorithms, respectively. These findings highlight the potential of population-level center-based sampling to enhance the performance of meta-heuristic algorithms.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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 it