Opposition-Based Crossover Operation for Differential Evolution Algorithm
Bibliographic record
Abstract
Differential Evolution (DE) is widely recognized as an effective, robust, and gradient-free global optimization algorithm. However, the DE algorithm's search strategy has certain limitations that present opportunities for further improvement. Opposition-based Learning (OBL) as one of the efficient computational concepts provides the optimizer with the capability of exploring the search space in opposite directions. This research paper introduces a novel crossover scheme for the DE algorithm based on OBL concept. Unlike existing approaches in the literature, which primarily focus on utilization of OBL in population level, proposed scheme takes the advantage of OBL in operation level. In proposed scheme, the crossover operator generates two trial vectors in opposite directions, enhancing the exploration capability of the search strategy and taking a cautious approach by regularly examining the opposite directions during crossover. To evaluate the effectiveness of the proposed method, a series of experiments are conducted using the CEC-2017 benchmark functions with two different numbers of dimensions: 30 and 50. The results demonstrate a significant improvement in performance of the DE algorithm through the proposed method.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".