Gaussian mixture modeling for dynamic particle swarm optimization of recurrent problems
Bibliographic record
Abstract
In dynamic optimization problems, the optima location and fitness value change over time. Techniques in literature for dynamic optimization involve tracking one or more peaks moving in a sequential manner through the parameter space. However, many practical applications in, e.g., video and image processing involve optimizing a stream of recurrent problems, subject to noise. In such cases, rather than tracking one or more moving peaks, the focus is on managing a memory of solutions along with information allowing to associate these solutions with their respective problem instances. In this paper, Gaussian Mixture Modeling (GMM) of Dynamic Particle Swarm Optimization (DPSO) solutions is proposed for fast optimization of streams of recurrent problems. In order to avoid costly re-optimizations over time, a compact density representation of previously-found DPSO solutions is created through mixture modeling in the optimization space, and stored in memory. For proof of concept simulation, the proposed hybrid GMM-DPSO technique is employed to optimize embedding parameters of a bi-tonal watermarking system on a heterogeneous database of document images. Results indicate that the computational burden of this watermarking problem is reduced by up to 90.4% with negligible impact on accuracy.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".