Real/Binary-Like Coded Genetic Algorithm to Automatically Generate Fuzzy Knowledge Bases
Bibliographic record
Abstract
This paper presents the results of the implementaion of a combination of a real-coded and binary-like coded genetic algorithm (RBLGA) to automatically generate fuzzy knowledge bases (FKB) from a set of numerical data. The algorithm allows one to fulfil a contradictory paradigm in term of FKB precision and simplicity (high precision generally translates into high complexity level) considering a randomly generated population of potential FKBs. The RBLGA is divided in two principal coding ways: 1) a real coded genetic algorithm (RCGA) that maps the fuzzy sets repartition and number (which drives the number of fuzzy rules) into a set of real numbers and. 2) a binary like aenetic algorithm that deals with the fuzzy rule base (a set of integer numbers). The RBLGA uses three reproduction mechanisms, a BLX-α, a simple crossover and a fuzzy set reducer. The RBLGA is validated through a theoretical surface and, funally, applied to a set of experimental data.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".