Combining Competitive And Cooperative Coevolution For Training Cascade Neural Networks
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Bibliographic record
Abstract
Cooperative Coevolution (CC) has been shown to be effective in problems where certain arcbitectural details of the solution are evolved. This is the case of cascade neural networks where the number of bidden units is not pre-established but rather emerges through learning. We take a step towards having coadapted subcomponents emerge rather than being hand designed by showing that competing populations (evolved by GAs with different mutation and crossover probabilities) can be successfully used in selecting the species that are subsequently coevolved in a cooperative model. Our experimental results indicate that retraining is an essential step in the cooperative coevolution model. Previous studies used evolutionary algorithms (EAs) to train connection weights and neuron thresholds in artificial neural networks (ANNs). We show that by also evolving the characteristics of the neurons themselves, the quality of the solution (in terms of number of hidden units) could be significantly improved.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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