{"id":"W2809759906","doi":"10.1016/j.advengsoft.2018.06.001","title":"Multi-surrogate-based Differential Evolution with multi-start exploration (MDEME) for computationally expensive optimization","year":2018,"lang":"en","type":"article","venue":"Advances in Engineering Software","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"National Natural Science Foundation of China","keywords":"Kriging; Surrogate model; Mathematical optimization; Global optimization; Optimization problem; Computer science; Differential evolution; Meta-optimization; Radial basis function; Algorithm; Mathematics; Artificial intelligence; Artificial neural network; 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":[0.001233165,0.0007848458,0.001100064,0.0008228524,0.000455264,0.0006887367,0.001133009,0.001631413,0.002959743],"category_scores_gemma":[0.003639165,0.0005692339,0.0009136625,0.0008240619,0.0006205622,0.0007840859,0.001870948,0.001841573,0.0004854264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005289186,"about_ca_system_score_gemma":0.0008348583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00135132,"about_ca_topic_score_gemma":0.001591655,"domain_scores_codex":[0.9994773,0.000231773,0.00002279053,0.00004573628,0.000186484,0.00003597037],"domain_scores_gemma":[0.998801,0.0008236289,0.00007233548,0.00009187686,0.0001507588,0.00006037852],"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":[0.00007010004,0.0000636897,0.0002537559,0.0001049685,0.00003663221,0.00005168493,0.000052124,0.9483624,0.001727449,0.01449861,0.0008257068,0.03395293],"study_design_scores_gemma":[0.000004787729,0.00001613631,0.00002133603,0.000006998474,0.000002454539,0.00000782417,0.000002013307,0.9977674,0.0002170503,0.001511286,0.000440376,0.00000237506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009925312,0.0004654618,0.9839616,0.0001300867,0.00009072322,0.00004731686,0.00003757142,0.0002529251,0.00508909],"genre_scores_gemma":[0.3444165,0.0003959942,0.6498605,0.0002317026,0.00005996617,0.0004324324,0.0001971576,0.0002618515,0.004143856],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002959743,"threshold_uncertainty_score":0.009901285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01604897834074174,"score_gpt":0.2640538693778125,"score_spread":0.2480048910370708,"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."}}