Heterogeneous Multi-Population Cultural Algorithm
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this article, a new architecture for Cultural Algorithms is proposed. The new architecture incorporates a number of sub-populations such that each sub-population is designed to optimize different parameters. According to the assigned parameters, each sub-population is a set of partial solutions which are managed by a local CA. Local CAs do not communicate with each other directly. In this architecture, a shared belief space is considered to record the best parameters. Local CAs send their best partial solutions to the belief space every generation. The belief space then updates its record of best parameters which will be used later by local CAs to evaluate their partial solutions. Due to incorporating a number of heterogeneous sub-populations, the proposed architecture is called Heterogeneous Multi-Population Cultural Algorithm (HMP-CA). Additionally, a local search heuristic is proposed to speed up the convergence of HMP-CA. The proposed HMP-CA is evaluated using a number of numerical optimization benchmark functions. The results show that the HMP-CA without the local search offers competitive results compared to the state-of-the-art methods and incorporating the proposed local search heuristic makes the proposed HMP-CA more efficient such that it outperforms all the state-of-the-art methods.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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