Optimizing Particle Swarm Optimization 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
Particle Swarm Optimization (PSO) algorithm has become more popular recently. It has been shown to be an effective optimization tool in most of the applications. In this paper, we have applied the PSO algorithm to a sample Artificial Neural Network (ANN) application, measured the improvement, and optimized the PSO parameters to improve results as much as possible. The application is character recognition of English numbers. Two indicators of accuracy of the results and processing time are taken in to account. The objective of this paper is to show that we can empirically adjust the PSO parameters to optimize PSO for the best results. Through several iterative processes of extracting improvements and adjusting the PSO parameters, we have recorded optimized PSO parameters and respective variances for similar applications. Indeed, the method can also be extended to alphabetic characters by just providing the input training patterns of each character. The details of the proposed approach and the simulation results are recorded in this paper.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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