{"id":"W3030155800","doi":"10.1016/j.asoc.2020.106429","title":"Kriging-assisted Discrete Global Optimization (KDGO) for black-box problems with costly objective and constraints","year":2020,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"Fundamental Research Funds for Central Universities of the Central South University; Fundamental Research Funds for the Central Universities; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Kriging; Mathematical optimization; Computer science; Robustness (evolution); Black box; Underwater glider; Benchmark (surveying); Sampling (signal processing); Algorithm; Mathematics; Artificial intelligence; 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"],"consensus_categories":[],"category_scores_codex":[0.0001706313,0.0003238028,0.0003502677,0.00004959678,0.0003678498,0.0002661509,0.0003924289,0.00008554587,0.000003391175],"category_scores_gemma":[0.00009796111,0.0003091817,0.00004584326,0.0007348428,0.0003240401,0.0003428417,0.0002849995,0.0001694442,0.000004566797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001114836,"about_ca_system_score_gemma":0.0001270725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004259207,"about_ca_topic_score_gemma":0.000002390821,"domain_scores_codex":[0.9979429,0.00004271249,0.0003527937,0.0009338678,0.0002766882,0.0004510218],"domain_scores_gemma":[0.9986658,0.0002529639,0.0003250267,0.0002432527,0.0002834448,0.0002295443],"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":[0.0000496545,0.00003240356,0.0002210337,0.00005387992,0.00006114505,0.000004128688,0.001742185,0.9452683,0.0001290119,0.01296259,0.00002333599,0.03945236],"study_design_scores_gemma":[0.002230779,0.0001586328,0.0002752699,0.00004851608,0.00002164105,0.00002319934,0.0003748943,0.9955695,0.0002390055,0.0005924443,0.0000638429,0.0004023248],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000600118,0.00002453034,0.9949993,0.0004533752,0.00007481761,0.001408715,0.00002009388,0.0005480479,0.001870979],"genre_scores_gemma":[0.4416029,0.000002087336,0.5577892,0.0004931159,0.00004478313,0.00002646346,0.00001756137,0.00002026304,0.000003577799],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4410028,"threshold_uncertainty_score":0.999936,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01456537642046512,"score_gpt":0.2493201298439894,"score_spread":0.2347547534235243,"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."}}