Footprinting the Behaviour of Particle Swarm Optimization with Increasing Dimensionality
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
It is well documented that the performance of Particle Swarm optimization changes (deteriorates) with increasing dimensionality of the search space. It is less well documented that the operational behaviour of Particle Swarm optimization (PSO) can also change with increasing dimensionality. The current study documents these changes by using Self-Organizing Maps to “footprint” the operation of PSO. Increasing dimensionality produces key changes to the footprints in a multi-modal search space, but these changes do not occur in a unimodal search space. A deeper analysis is then conducted to connect the observed changes in footprints in multi-modal search spaces to changes in the operational behaviour of PSO caused by the effects of increasing dimensionality. The collected data indicate a correlation between the performance degradation of PSO and the decreased rates of success of exploratory moves, and this trend can be isolated from the effects of the exponentially increasing search space volumes that are produced in higher dimensions for continuous domain search spaces.
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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 | 0.002 | 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 | 0.000 |
| 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