{"id":"W1511840833","doi":"","title":"A global optimal technique based on Moving Least Square and Improved Differential Evolution","year":2008,"lang":"en","type":"article","venue":"International Conference on Electrical Machines and Systems","topic":"Antenna Design and Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Benchmark (surveying); Differential evolution; Mathematical optimization; Global optimization; Square (algebra); Inverse; Computer science; Algorithm; Function (biology); Differential (mechanical device); Inverse problem; Surface (topology); Electromagnetics; Mathematics; Engineering","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":[0.0007741252,0.0007717354,0.0009414088,0.0007569239,0.0003438775,0.0004463894,0.0009407336,0.0008879691,0.001532078],"category_scores_gemma":[0.001441363,0.0003987067,0.001159124,0.000664853,0.0005687285,0.001005624,0.0007976499,0.0008760598,0.0004367893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003590831,"about_ca_system_score_gemma":0.0005262152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001047063,"about_ca_topic_score_gemma":0.00087989,"domain_scores_codex":[0.9994843,0.0001094271,0.00002222047,0.00009524019,0.0002634315,0.00002543484],"domain_scores_gemma":[0.9996834,0.0001367736,0.00003384032,0.00004794323,0.00008606662,0.0000120648],"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":[0.00006929114,0.00005089027,0.000733679,0.0002577188,0.0001244015,0.0001293658,0.0001993611,0.6070867,0.03135967,0.05672842,0.002354751,0.3009058],"study_design_scores_gemma":[0.000009659591,0.0000537935,0.0001353492,0.000008214502,0.0000163534,0.00006708814,0.000007152636,0.9884389,0.003499682,0.003413325,0.004334975,0.0000154498],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001588554,0.0001030133,0.9975213,0.00002816918,0.00002457241,0.000008477416,0.000004371481,0.0001016486,0.0006198728],"genre_scores_gemma":[0.1317628,0.0003108252,0.8642344,0.00009509696,0.00006071445,0.0001286679,0.00006402678,0.0001832683,0.003160259],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001532078,"threshold_uncertainty_score":0.005125284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01360823923323994,"score_gpt":0.2293321126024335,"score_spread":0.2157238733691935,"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."}}