Estimation of Distribution using Population Queue based Variational Autoencoders
Why this work is in the frame
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Bibliographic record
Abstract
We present a new Estimation of Distribution algorithms (EDA) based on two novel Variational Autoencoders generative model building algorithms. The first method, Variational Autoencoder with Population Queue (VAE-EDA-Q), employs a queue of historical populations, which is updated at each iteration of EDA in order to smooth the data generation process. The second method uses Adaptive Variance Scaling (AVS) with VAE-EDA-Q to dynamically update the variance at which the probabilistic model is sampled. The results obtained prove our methods to be significantly more computationally efficient than state-of-the-art algorithms and perform significantly less number of fitness evaluations when tested on benchmark problems such as Trap-k and NK Landscapes. Moreover, we report results of applying our approach successfully to highly complex problems such as Trap 11, Trap 13, and NK Landscapes with neighborhood size K = 8 and K = 10.
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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