Opposition-based Differential Evolution with protective generation jumping
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
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 N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> fittest 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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