USING COMPETITIVE CO-EVOLUTION TO EVOLVE BETTER PATTERN RECOGNISERS
Why this work is in the frame
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Bibliographic record
Abstract
We present a system for the automatic synthesis of classifiers. The CellNet system for generating binary pattern classifiers is used as a base for further experimentation. As in the original CellNet software, we evolve pattern recognisers (hunters). However, in this version called CellNet Co-Ev, we also evolve the patterns (prey) in a competitive co-evolution. Patterns evolve through the application of camouflage functions, which are used to obscure the data naturally found in the database. The addition of this competitive co-evolution yields a larger and more varied database, artificially increasing the difficulty of the classification task. Application to the CEDAR database of handwritten characters shows an increase in the reliability of the evolution of recognisers, as well as in the elimination of over-fitting, relative to the original CellNet software.
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 | 0.001 |
| Open science | 0.001 | 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