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Record W2478279759

A hybrid genetic algorithm for the Generalized Traveling Salesman Problem

2001· article· en· W2478279759 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGenetic and Evolutionary Computation Conference · 2001
Typearticle
Languageen
FieldComputer Science
TopicMetaheuristic Optimization Algorithms Research
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsTravelling salesman problemHeuristicsVertex (graph theory)Bottleneck traveling salesman problemAlgorithmComputer scienceBenchmark (surveying)Genetic algorithm2-optLin–Kernighan heuristicMathematical optimizationSelection (genetic algorithm)Christofides algorithmMathematicsGraphCombinatoricsTheoretical computer scienceArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

The Generalized Traveling Salesman Problem consists of determining a shortest tour on a graph passing through each of several clusters of vertices. A hybrid genetic algorithm (GA) is developed to solve a variant of this problem where exactly one vertex must be visited in each cluster. In this algorithm, the GA searches for a good selection of vertices, while classical operations research techniques are used to produce a tour with the selected vertices. Numerical results are reported on a standard set of benchmark problems and a comparison is provided with the two best heuristics reported in the literature.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.882
Threshold uncertainty score0.668

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.035
GPT teacher head0.278
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it