{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002020888,0.0004061757,0.0005322555,0.0002290212,0.000574094,0.001058206,0.0008065265,0.0002362846,0.000001259276],"category_scores_gemma":[0.0001239106,0.0003866678,0.0001833961,0.0003352172,0.00005105187,0.0004684,0.00005128785,0.00009556171,0.00007764667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001545464,"about_ca_system_score_gemma":0.0001623526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004878304,"about_ca_topic_score_gemma":0.000002578876,"domain_scores_codex":[0.9965028,0.0005402178,0.00100343,0.0009102575,0.0003913776,0.0006519387],"domain_scores_gemma":[0.9969003,0.0004485458,0.0006556701,0.0009534099,0.0008061621,0.0002359809],"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":[0.00007149861,0.0003970281,0.000009833861,0.0004822551,0.0002174744,0.00000563394,0.002255035,0.968124,0.005272325,0.009716916,0.001520971,0.01192699],"study_design_scores_gemma":[0.001641886,0.001306165,0.00002226133,0.0002264304,0.00002370087,0.00008340342,0.000107297,0.993889,0.0008766872,0.0002049865,0.001152021,0.000466108],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002389584,0.000190924,0.9876441,0.00002877994,0.004115828,0.004267213,0.0001290779,0.00121017,0.00002434515],"genre_scores_gemma":[0.6829268,0.000001017606,0.3147534,0.00007189745,0.0006749643,0.001214212,0.0001525263,0.00005046154,0.0001547818],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6805372,"threshold_uncertainty_score":0.9999788,"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."}}