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Record W2097812145 · doi:10.1109/lcomm.2002.805515

A memetic algorithm for assigning cells to switches in cellular mobile networks

2002· article· en· W2097812145 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

VenueIEEE Communications Letters · 2002
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsMemetic algorithmTabu searchComputer scienceHeuristicsCellular networkHeuristicContext (archaeology)MemeticsAlgorithmLocal search (optimization)Mathematical optimizationArtificial intelligenceMathematicsComputer network

Abstract

fetched live from OpenAlex

Assigning cells to switches in cellular mobile networks is an NP-hard problem which, for real size mobile networks, could not be solved using exact methods. In this context, heuristic approaches like memetic algorithms can be used. This paper proposes a memetic algorithm (MA) to solve this problem. The implementation of this algorithms has been subject to extensive tests. The results obtained confirm the efficiency and the effectiveness of MA to provide good solutions for moderate- and large-sized cellular mobile networks, in comparison with tabu search and Merchant and Sengupta's heuristics. This heuristic can be used to solve NP-hard problems, like designing and planning, in the next-generation mobile networks.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.684
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0070.001
Research integrity0.0000.001
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 head0.051
GPT teacher head0.289
Teacher spread0.238 · 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