Study of estimation of distribution algorithms applied to neuroevolution
Bibliographic record
Abstract
This thesis proposes a methodology for the automatic design of neural networks via Estimation ofDistribution Algorithms (EDA). The method evolves both topology and weights. To do so, topol-ogy is represented with a fixed-length, indirect encoding; weights are represented as a bitwise en-coding. The topology and weights are searched via an incremental learning algorithm and a GuidedMutation operator. To explore suitable EDA ensembles, the study presented here interchangeablycombined two representations for topology, two for weights, and two learning algorithms. Testsused in the analysis include: XOR, 6-bit Multiplexer, Pole-Balancing, and the Retina Problem. Theresults demonstrate that: (1) the Guided Mutation operator accelerates optimization on problemswith a fixed fitness function; (2) the EDA approach introduced here is competitive with similarGP methods and is a viable method for Neuroevolution; (3) our methodology scales well to harderproblems and automatically discovers modularity.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".