{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005171635,0.0001681748,0.0001617968,0.0001117265,0.00008799949,0.00008046631,0.00008965269,0.00009897826,0.00001872923],"category_scores_gemma":[0.0000301366,0.0001424316,0.00003327978,0.0001078284,0.00003202535,0.00007741097,0.00001275906,0.0001366857,0.000002404165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001199287,"about_ca_system_score_gemma":0.00002281008,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008480268,"about_ca_topic_score_gemma":0.000004443944,"domain_scores_codex":[0.9992031,0.00003654185,0.0001934097,0.0002087085,0.0001994418,0.0001588051],"domain_scores_gemma":[0.9996941,0.00003277507,0.00003885146,0.00007279966,0.00008670239,0.00007474818],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002736262,0.000922833,0.09172565,0.0005058543,0.0004399973,0.0001751867,0.0003370724,0.3742342,0.07896462,0.3884654,0.001299759,0.06019313],"study_design_scores_gemma":[0.000385592,0.0001968127,0.008782846,0.00006499627,0.000005452069,0.00005560089,0.000006482503,0.990196,0.00002099031,0.0001176459,0.00001624998,0.0001513402],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1051861,0.000155659,0.8895476,0.000116703,0.0003475947,0.0003676508,0.00004356294,0.0002158157,0.004019305],"genre_scores_gemma":[0.9993935,0.00005709052,0.0002233311,0.00002388403,0.0001149704,0.00004843063,0.00002450655,0.00001261496,0.0001016269],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8942075,"threshold_uncertainty_score":0.5808192,"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."}}