An evolutionary algorithm for identifying faults in t-diagnosable systems
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 paper describes a novel approach to the problem of system-level fault diagnosis using genetic algorithms. Consider a system composed of n independent units, each of which tests a subset of the others. It is assumed that at most t of these units are permanently faulty. Such a system is said to be t-diagnosable if, given any complete collection of test results, the set of faulty units can be uniquely identified. Genetic algorithms have recently received much attention as a class of robust stochastic search algorithms for various optimization problems. An efficient method based on evolutionary algorithms is developed to solve the diagnosis problem. The representation of the search space used is in the form of a binary vector of length n. Each bit indicates the status (faulty or fault-free) of its corresponding unit. Genetic operators are adapted to the context of system-level diagnosis. The genetic algorithm was implemented and tested on random test graphs. The simulation results demonstrate the efficiency of the proposed diagnosis algorithm.
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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 | 0.001 |
| 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