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Record W4255316375 · doi:10.35940/ijitee.a4215.119119

BOSA: Binary Orientation Search Algorithm

2019· article· en· W4255316375 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

VenueInternational Journal of Innovative Technology and Exploring Engineering · 2019
Typearticle
Languageen
FieldComputer Science
TopicMetaheuristic Optimization Algorithms Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBenchmark (surveying)Orientation (vector space)Binary numberAlgorithmComputer scienceBinary search algorithmOptimization algorithmSearch algorithmMathematical optimizationMathematicsArithmetic

Abstract

fetched live from OpenAlex

Optimization algorithms have many applications in solve the optimization problems in various sciences. The Orientation Search Algorithm (OSA) is an optimizer that is simulated the rules of a game called orientation game. In OSA searcher agent are players of orientation game that moves on the playground depending on the direction of the referee. in this study, binary model of OSA which called Binary Orientation Search Algorithm (BOSA) is proposed. BOSA and eight other algorithms (BGA, BPSO, BGSA, BGOA, BBA, BMOA, and BDA) are tested on twenty-three Benchmark test function. Proposed BOSA has a high ability to solve optimization problems compared to other algorithms according to the evaluation results.

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: none
Teacher disagreement score0.878
Threshold uncertainty score0.363

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.024
GPT teacher head0.280
Teacher spread0.256 · 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