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Record W2394358333

A Technique of Implementing Genetic Algorithm by Bit-Operation

2002· article· en· W2394358333 on OpenAlex
Jia Chen

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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMetaheuristic Optimization Algorithms Research
Canadian institutionsCAE (Canada)
Fundersnot available
KeywordsCrossoverComputer scienceDecimalBinary numberGenetic algorithmInteger (computer science)AlgorithmEncoding (memory)Binary codeMutationInteger programmingTransformation (genetics)Code (set theory)ArithmeticParallel computingSet (abstract data type)MathematicsProgramming languageArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

Genetic Algorithm(GA)is known as a potential technique to solve nonlinear optimization problem.The searching speed and computing effectiveness of GA depend greatly upon programming skills.Encoding and storing are foundational techniques which influence directly efficiency of crossover and mutation especially for a large amount data.The paper brings out the way of access parameters in binary codes immediately,in which one or more decimal parameters are accommodated in an unsigned integer or array.Because each storing unit:bit contains binary code 0 or 1,no need to apply transformation for parameters between decimal and binary.This kind of storing can reduce storage greatly and benefit to practice an effective route of genetic operation like crossover or mutation,that is,Bit Operation.Since the machine codes of an integer are binary,they can be operated directly by using Bit Operation.The Bit Operation cuts off some intermediate routes,so that the crossover and mutation operations may shorten rather time finally.

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 categoriesInsufficient payload (model declined to judge)
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 score0.948
Threshold uncertainty score0.999

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.267
Teacher spread0.248 · 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