CenDE: Centroid-Based Differential Evolution
Bibliographic record
Abstract
Differential Evolution (DE) is a popular global optimization algorithm, mostly due to its high performance, easy implementation, and utilization of a few control parameters. The mutation scheme is one of the important steps of DE, which selects a number of individuals from the population as parents to generate the next population during its evolutionary process. The parents are traditionally selected randomly and in some mutation schemes the best member of population is selected as one of the parents. In this paper, we propose the centroid-based differential evolution (CenDE) algorithm, which uses the centroid of top three individuals in the population in terms of objective function value performance as the base parent. The experiments are conducted for high and low dimensional problems with small and standard population sizes on CEC Black-Box Optimization Benchmark problems 2015 (CEC-BBOB 2015). Our experiments show that the center of best three individuals plays an important role in generating candidate individuals with better objective values for the next generation, resulting in a faster convergence of the DE algorithm.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".