{"id":"W2805879431","doi":"10.1007/s10617-018-9208-1","title":"ImGA: an improved genetic algorithm for partitioned scheduling on heterogeneous multi-core systems","year":2018,"lang":"en","type":"article","venue":"Design Automation for Embedded Systems","topic":"Distributed and Parallel Computing Systems","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Genetic algorithm; Scheduling (production processes); Distributed computing; Algorithm; Parallel computing; Mathematical optimization; Mathematics; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007285227,0.001116533,0.001128478,0.0007044745,0.0005320914,0.000681537,0.00156291,0.001056172,0.002466968],"category_scores_gemma":[0.001999267,0.0005106737,0.000709357,0.0006880302,0.0004595113,0.0006442932,0.0008610988,0.001074916,0.0004444691],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008458993,"about_ca_system_score_gemma":0.001802501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01027303,"about_ca_topic_score_gemma":0.008130725,"domain_scores_codex":[0.9996266,0.0001407005,0.00001257937,0.00005536943,0.00009915616,0.00006554622],"domain_scores_gemma":[0.9995634,0.0002198296,0.0000376998,0.00003922559,0.0001056573,0.00003415071],"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":[0.00008030858,0.00005054889,0.0002402855,0.00003808302,0.00003725671,0.00003123334,0.00003617572,0.951547,0.001513129,0.001825858,0.001123378,0.04347684],"study_design_scores_gemma":[0.00001792463,0.00002270273,0.00004267419,0.000003375347,0.000007198791,0.000005056541,0.000004556082,0.9989192,0.0001885107,0.0005027831,0.0002838744,0.000002111674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03375335,0.0004538231,0.9605088,0.0001605166,0.0001550621,0.0001056604,0.00006382338,0.001525999,0.003273157],"genre_scores_gemma":[0.415616,0.0002770892,0.5794834,0.0002595247,0.00008686466,0.0004337864,0.0002485572,0.0003768467,0.003217802],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01027303,"threshold_uncertainty_score":0.02042651,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07758551943139083,"score_gpt":0.313870968614591,"score_spread":0.2362854491832002,"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."}}