Discriminating and Clustering Ordered Permutations Using Neural Network and Potential Applications in Neural Network-Guided Metaheuristics
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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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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