Swine influenza inspired optimization algorithm and its application to multimodal function optimization and noise removal
Bibliographic record
Abstract
Swine Influenza Inspired Optimization (SIIO) is a search algorithm proposed for optimal solution. The authors followed the SIR (susceptible - infectious-recovered) virus spread model of Swine Influenza to develop the new evolutionary algorithm named as SIIO. SIR model is used to frame optimization algorithm following the spread and control phenomenon of the swine flu virus in the human population. The fitness based classes viz. susceptible (S), infectious (I) and recovered (R) of the individuals are made and treatment is used for the affected individuals by imitating the health information from the best fitness individual. The proposed algorithm shows improved performance on multi-dimensional unimodal and multimodal standard numerical benchmark functions than the compared optimization algorithms. The performance of the SIIO algorithm is better in terms of speed of convergence and quality of solutions. The SIIO is also applied for the Gaussian noise removal with Blind Source Separation (BSS) based on Independent Component Analysis (ICA).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".