Discriminating and Clustering Ordered Permutations Using Neural Network and Potential Applications in Neural Network-Guided Metaheuristics
Bibliographic record
Abstract
Adaptive Resonance Theory (ART) neural network has been used in many applications due to its fast-adaptable learning process and stable operations. In this work, we present a technique for discriminating and clustering ordered permutation using ART-1 and Improved-ART-1. In the process, we developed a novel technique for converting ordered permutations to binary vectors to cluster them using ART. The performances of ART-1 and Improved-ART-1 have been investigated, and the proposed binary conversion methods were evaluated under varying parameters and problem sizes. Three performance indicators, i.e., misclassification, cluster homogeneity, and average distance are considered in the analysis. The numerical results indicate the superiority of one of the proposed binary conversion techniques over the other and Improved-ART-1 over ART-1. Moreover, potential applications of the proposed technique in developing ANN guided metaheuristics to solve problems whose solutions are ordered permutations are discussed. A case study in solving flexible flow shop scheduling using ANN guided Genetic Algorithm is also presented.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 |
| 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".