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Record W2014633436 · doi:10.1145/1068009.1068279

Isolating the benefits of respect

2005· article· en· W2014633436 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMetaheuristic Optimization Algorithms Research
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCrossoverSimulated annealingComputer scienceGenetic algorithmFocus (optics)Adaptive simulated annealingMathematical optimizationAnnealing (glass)Distributed computingAlgorithmArtificial intelligenceMachine learningMathematicsMaterials sciencePhysics

Abstract

fetched live from OpenAlex

Abstract. The three mechanisms of crossover are transmission, assortment, and respect. Of these three mechanisms, assortment (i.e. recombination) is traditionally viewed as the primary feature and key advantage of crossover. However, respect (the preservation of common components) is also a feature that is unique to multi-parent operators like crossover – it takes two (or more) parents to have/identify common components. The effects of respect are isolated from all other aspects of genetic algorithms by using a parallel implementation of simulated annealing. In this implementation, the preservation of common components is used to focus the search process and this focus has improved the performance of simulated annealing on the TSP. Since only the mechanism of respect is transferred from genetic algorithms to simulated annealing, these experiments isolate and demonstrate the benefits of respect. 1

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 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 score0.917
Threshold uncertainty score0.244

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.037
GPT teacher head0.298
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