Opposition-based Differential Evolution with protective generation jumping
Bibliographic record
Abstract
The Opposition-based Differential Evolution (ODE) algorithm has shown to be superior to its parent, Differential Evolution (DE) algorithm in solving many real-world problems and benchmark functions efficiently. An acceleration component of ODE, called generation jumping, is involved with creating opposite population and competing with current population, and from the union of those populations, selecting the Npfittest individuals. The jumping is triggered based on a constant percentage (i.e., jumping rate) during search process. There are optimization problems in which generation jumping is not useful and only wastes computation time and resources. In this paper, we focus on those certain benchmark functions which ODE performs poorly because of the useless generation jumping, and we introduce Opposition-Based Differential Evolution with Protective Generation Jumping (ODEPGJ), in which it makes the ODE algorithm more adaptive in term of generation jumping. In fact, we stop generation jumping when it seems to be unhelpful in acceleration process. The experimental verifications are provided to show the improvement caused due to the mentioned protective generation jumping.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".