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Record W2123622158 · doi:10.1109/rtcsa.1999.811284

Hybrid genetic algorithms for scheduling partially ordered tasks in a multi-processor environment

2003· article· en· W2123622158 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicScheduling and Optimization Algorithms
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsTabu searchComputer scienceScheduling (production processes)Job shop schedulingMathematical optimizationFair-share schedulingDynamic priority schedulingGenetic algorithmQuality control and genetic algorithmsCombinatorial optimizationParallel computingOptimization problemAlgorithmTheoretical computer scienceDistributed computingMeta-optimizationMathematicsMachine learningSchedule

Abstract

fetched live from OpenAlex

Scheduling partially ordered tasks in a multiple-processor environment is a very complex combinatorial optimization problem. In this paper, hybrid genetic algorithms for the scheduling optimization problem are presented. We first present a non-string representation of the solutions for scheduling problems. Then we provide a hybrid mechanism for the choice of genetic operators. The issue of illegal solution is addressed as well. Experimental results for the choice of parameters and the comparison of GA and Tabu search are also presented.

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: Methods
Teacher disagreement score0.207
Threshold uncertainty score0.820

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 head0.026
GPT teacher head0.244
Teacher spread0.219 · 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

Quick stats

Citations54
Published2003
Admission routes1
Has abstractyes

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