Crossover and mutation in genetic algorithms employing canonical signed-digit number system
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, a novel approach is presented for the restoration of canonical signed-digit (CSD) numbers to their correct format after the application of crossover and mutation operations in genetic algorithms. The proposed approach has two main distinct features: First, it employs the same restoration process for both crossover and mutation operations, yielding the process as uniform. Second, it is based on a local, as opposed to global, restoration of the produced offspring numbers, making the corresponding computational implementation efficient. A new technique is also proposed to predict the illegal break-points for the crossover and the illegal bit-locations for the mutation operation, preventing the possibility of generating any out-of-range CSD numbers. An application example is given to illustrate the results.
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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