Creating Optimal Edit Metric Codes using a Genetic Algorithm
Bibliographic record
Abstract
Implementing error correcting codes is a challenging problem in information theory and the search for optimal codes, those of maximum size, has been the focus of research for years. Edit distance is defined as the minimum number of substitutions, insertions, and deletions required to change one word into another. Edit metric codes can be used to detect and correct substitution, insertion, and deletion errors from noise that occurs during transmission or storage of data. Important applications include those related to DNA storage, in which all these types of errors can occur. A (n, M, d)qedit metric code consists of a set of M q-ary codewords of length n where all codewords are at edit distance at least d apart. Such a code is optimal if M has the largest possible value given n, d, and q. Using a steady state genetic algorithm, this work attempts to increase the largest known value or minimum bound of M for which there exists a binary edit distance metric code with fixed codeword lengths. This work compares the ability of variation operators (two crossover and two mutation) to produce the best known values of M for a set of parameters. The results show that most combinations of variation operators are able to match the best known results for the parameter sets used in this study. For n = 16, the combination of variation operators are able to increase the best known lower bounds.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".