Population-level center-based sampling for meta-heuristic algorithms
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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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".