{"id":"W2123622158","doi":"10.1109/rtcsa.1999.811284","title":"Hybrid genetic algorithms for scheduling partially ordered tasks in a multi-processor environment","year":2003,"lang":"en","type":"article","venue":"","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Tabu search; Computer science; Scheduling (production processes); Job shop scheduling; Mathematical optimization; Fair-share scheduling; Dynamic priority scheduling; Genetic algorithm; Quality control and genetic algorithms; Combinatorial optimization; Parallel computing; Optimization problem; Algorithm; Theoretical computer science; Distributed computing; Meta-optimization; Mathematics; Machine learning; Schedule","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001719026,0.0001940343,0.0001910427,0.0001023134,0.0000533272,0.00004272583,0.0001088971,0.00007274721,0.0001763993],"category_scores_gemma":[0.00008316425,0.0002011326,0.00005434233,0.0001118532,0.00001967439,0.00007058126,0.00001049684,0.00011353,0.00005176992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006422912,"about_ca_system_score_gemma":0.00002785207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005908571,"about_ca_topic_score_gemma":0.000009510834,"domain_scores_codex":[0.9988738,0.00001966495,0.0003385183,0.0002697131,0.0001256031,0.0003726585],"domain_scores_gemma":[0.9996132,0.00004416967,0.00002764789,0.0001870306,0.00002504097,0.0001028936],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003524272,0.00005724079,0.0002312017,0.00003308405,0.00001830067,0.000004370893,0.00009289498,0.9934193,0.0003007498,0.00006164664,0.00001638658,0.005761261],"study_design_scores_gemma":[0.001484691,0.00002463966,0.0001742927,0.00001450932,0.00001195771,0.000006869308,0.00009945321,0.9860776,0.01066189,0.0001030194,0.001063734,0.0002773362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02858614,0.0006065208,0.9696685,0.00003353805,0.0002282528,0.0003747565,0.000008588599,0.0002251767,0.0002685009],"genre_scores_gemma":[0.2358901,0.000106259,0.7634876,0.00005854386,0.00003247923,0.0001609851,0.0000130582,0.00004942072,0.0002015552],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2073039,"threshold_uncertainty_score":0.820195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02553000264354821,"score_gpt":0.2443336858314534,"score_spread":0.2188036831879052,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}