{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001002553,0.0002964229,0.0002411319,0.0003128749,0.0001746819,0.00007959674,0.0003389495,0.00008832889,0.000005507164],"category_scores_gemma":[0.0003484954,0.0003039285,0.00005025578,0.0006228719,0.00008235023,0.002519441,0.00006032656,0.0001265788,0.000005316508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003913308,"about_ca_system_score_gemma":0.00009617301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004026842,"about_ca_topic_score_gemma":0.00005248264,"domain_scores_codex":[0.9983366,0.00003750824,0.000351823,0.0006214545,0.0002848647,0.0003677839],"domain_scores_gemma":[0.9984691,0.0002593698,0.0001863912,0.0003130285,0.0006882143,0.00008385279],"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":[0.00004019085,0.0001089216,0.0004118704,0.00003969119,0.00000968569,0.000002743608,0.0003065448,0.9964774,0.000111649,0.0007546587,0.000002642545,0.001734023],"study_design_scores_gemma":[0.002923248,0.0002052962,0.0008702943,0.0001166331,0.000006791688,0.000002979378,0.00004963685,0.9944301,0.0007733976,0.0001376519,0.000108411,0.0003755592],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001989706,0.0001404575,0.9975418,0.00005489661,0.0006705784,0.0008054734,0.00001980863,0.0005659018,0.000002070381],"genre_scores_gemma":[0.1381971,0.00001281215,0.861187,0.00005028582,0.00008977022,0.0002879499,0.0001169036,0.00004390447,0.00001425021],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1379981,"threshold_uncertainty_score":0.9999413,"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."}}