A Genetic Algorithm with cycle representation and contraction digraph model for Guideway Network design of Personal Rapid Transit
Bibliographic record
Abstract
In this paper, we propose a steady-state genetic algorithm (GA) with cycle-based representation and a contraction digraph model to deal with the guideway network design problem of personal rapid transit (PRT). PRT is a novel transportation paradigm, where many computer-controlled vehicles running on an elevated guideway network (GN). A GN may contain hundreds of guideway links and how to design the minimum-cost feasible GN is a challenging problem. Given a set of stations, the proposed GA models a candidate GN as a union of one or more simple directed cycles visiting two or more stations. This cycle representation not only provides high solution locality but allows us to establish a contraction digraph model, where its feasibility can be efficiently evaluated. We also develop special genetic operators well suited for the cycle representation. Numerical experiments conducted for various problem instances show the proposed GA outperforms the conventional ones once the solution is represented by a moderate number of cycles.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".