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Record W2076906193 · doi:10.1142/s1469026805001611

A GENETIC ALGORITHM FOR THE DESIGN OF MINIMUM-COST TWO-CONNECTED NETWORKS WITH BOUNDED RINGS

2005· article· en· W2076906193 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInternational Journal of Computational Intelligence and Applications · 2005
Typearticle
Languageen
FieldEngineering
TopicVLSI and FPGA Design Techniques
Canadian institutionsBrock UniversityUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTabu searchComputer scienceBounded functionGenetic algorithmAlgorithmMathematical optimizationNetwork planning and designConstraint (computer-aided design)Ring (chemistry)Enhanced Data Rates for GSM EvolutionMathematicsArtificial intelligenceMachine learningComputer network

Abstract

fetched live from OpenAlex

This paper presents a genetic algorithm for designing minimum-cost two-connected networks such that the shortest cycle to which each edge belongs to does not exceed a given length. We provide numerical results based on randomly generated graphs found in the literature and compare the solution quality with that of tabu search and branch and bound. The results demonstrate the effectiveness of our algorithm and show promise for tackling ring-based network design problems. This paper is among the first to document the implementation of a genetic algorithm for the design of two-connected networks with the added constraint of bounded rings.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.669
Threshold uncertainty score0.297

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.020
GPT teacher head0.280
Teacher spread0.260 · 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