{"id":"W3028365645","doi":"10.1016/j.swevo.2020.100713","title":"Surrogate-assisted grey wolf optimization for high-dimensional, computationally expensive black-box problems","year":2020,"lang":"en","type":"article","venue":"Swarm and Evolutionary Computation","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":82,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"Fundamental Research Funds for the Central Universities; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Surrogate model; Robustness (evolution); Global optimization; Computation; Optimization problem; Local search (optimization); Mathematical optimization; Artificial intelligence; Radial basis function; Algorithm; Data mining; Machine learning; Mathematics; Artificial neural network","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002177823,0.0006264228,0.001692571,0.0005195975,0.0004160538,0.001085902,0.0009508899,0.001902816,0.002729967],"category_scores_gemma":[0.004397199,0.000548479,0.0006363164,0.0008050063,0.0009793269,0.0009471943,0.001282899,0.001459703,0.0004197794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005006488,"about_ca_system_score_gemma":0.0009957146,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001309353,"about_ca_topic_score_gemma":0.001105884,"domain_scores_codex":[0.9993758,0.0003328667,0.00002076347,0.00004042049,0.0001813789,0.00004892279],"domain_scores_gemma":[0.9985013,0.001084152,0.00009079655,0.00008491562,0.0001719623,0.00006687961],"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":[0.00005451023,0.0000279458,0.000134775,0.0000676976,0.000021297,0.00004653871,0.00002191879,0.9776405,0.0009078897,0.009472525,0.0005782939,0.01102616],"study_design_scores_gemma":[0.00000392,0.00000849278,0.00001775298,0.000003707504,0.000001449667,0.000004724478,0.000001574555,0.9977549,0.0001317858,0.001905323,0.0001651681,0.00000126695],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01535972,0.0004197533,0.9792063,0.0002326738,0.00006109463,0.00003192756,0.00003368115,0.0001595054,0.004495221],"genre_scores_gemma":[0.6269721,0.0006035681,0.3641323,0.0002069601,0.00007231691,0.0002860443,0.0001923563,0.0002136908,0.007320587],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002729967,"threshold_uncertainty_score":0.01151752,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02333119363072437,"score_gpt":0.251606041909398,"score_spread":0.2282748482786736,"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."}}