{"id":"W2082820221","doi":"10.1115/detc2008-49991","title":"Enhanced Multi-Agent Normal Sampling Technique for Global Optimization","year":2008,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; University of Manitoba","funders":"","keywords":"Mathematical optimization; Computer science; Sampling (signal processing); Global optimization; Standard deviation; Normal distribution; Algorithm; Mathematics; Statistics","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":[0.0009905745,0.0007814406,0.0009317061,0.000709113,0.0003406727,0.0005330479,0.00102249,0.0005442757,0.002118452],"category_scores_gemma":[0.001620976,0.0003038421,0.0008951775,0.0006229289,0.0005044434,0.0008788749,0.0008187224,0.001046089,0.0004328004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005084666,"about_ca_system_score_gemma":0.0007680715,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002278558,"about_ca_topic_score_gemma":0.00202915,"domain_scores_codex":[0.9992861,0.0002458558,0.00002236753,0.00007859943,0.0003253484,0.00004169312],"domain_scores_gemma":[0.9995016,0.0002064053,0.00005279162,0.00006809166,0.0001484381,0.00002263323],"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":[0.00008054385,0.00005044724,0.0005482673,0.0001333568,0.00006504129,0.00008675742,0.00008569352,0.8303897,0.007566363,0.02179841,0.001534123,0.1376613],"study_design_scores_gemma":[0.000007625062,0.00003955504,0.00007011436,0.00000455065,0.000006283125,0.00002748379,0.000004249673,0.9949782,0.0009100488,0.001887412,0.002059076,0.000005300457],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003057387,0.0001613587,0.9948211,0.00003308092,0.0000381366,0.00002952082,0.00001072175,0.0001608385,0.001687827],"genre_scores_gemma":[0.2807696,0.0004199142,0.7136062,0.00008506248,0.0000955559,0.0002361457,0.0001318834,0.0001649514,0.004490772],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002278558,"threshold_uncertainty_score":0.007086873,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07399700246656397,"score_gpt":0.3443902893398757,"score_spread":0.2703932868733117,"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."}}