{"id":"W3180164923","doi":"10.1007/s10589-022-00381-z","title":"Quantifying uncertainty with ensembles of surrogates for blackbox optimization","year":2022,"lang":"en","type":"article","venue":"Computational Optimization and Applications","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis","funders":"Institut de Valorisation des Données","keywords":"Mathematical optimization; Computer science; Stochastic optimization; Context (archaeology); Optimization problem; Metaheuristic; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001962849,0.0001586374,0.0001922913,0.0002251148,0.0007092764,0.00007312562,0.0003049031,0.00003048493,0.00003337223],"category_scores_gemma":[0.00003245445,0.0001657413,0.00004424689,0.0009091331,0.0001009256,0.0003688357,0.0001492695,0.00008263627,6.151138e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007212662,"about_ca_system_score_gemma":0.0001427125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004061205,"about_ca_topic_score_gemma":0.000001767677,"domain_scores_codex":[0.9986576,0.00006842273,0.0003551473,0.0004514765,0.0003027462,0.000164618],"domain_scores_gemma":[0.9982921,0.0004029851,0.0003431841,0.0002341328,0.0006541887,0.0000734125],"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":[0.00001743577,0.000105255,0.0001003749,0.000017838,0.0000204825,1.536968e-7,0.0001819184,0.9042312,0.00001109484,0.09348658,0.00002381459,0.001803856],"study_design_scores_gemma":[0.0009216107,0.0001051149,0.00006670846,0.000006795649,0.00001495,0.00001713026,0.0002229922,0.9959671,0.00004867593,0.001766694,0.0006701176,0.0001920445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00009161697,0.00008618096,0.9978354,0.0005145292,0.00004065679,0.001037482,0.00008652782,0.0001416532,0.0001659808],"genre_scores_gemma":[0.1127647,0.00002560717,0.8855883,0.0001455561,0.00002162754,0.0008936907,0.0004900365,0.00002152561,0.00004888744],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1126731,"threshold_uncertainty_score":0.6758732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02284280005294645,"score_gpt":0.2809344558812623,"score_spread":0.2580916558283158,"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."}}