{"id":"W1875588522","doi":"10.1109/aps.2005.1551736","title":"Antenna optimization using a hybrid evolutionary programming method","year":2005,"lang":"en","type":"article","venue":"","topic":"Antenna Design and Optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Evolutionary programming; Genetic programming; Evolutionary algorithm; Evolutionary computation; Genetic algorithm; Class (philosophy); Adaptation (eye); Artificial intelligence; Machine learning; Physics","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":[0.0003183529,0.0004078957,0.0005520023,0.0004050572,0.0002518748,0.0005963503,0.0007606637,0.0009002215,0.001965471],"category_scores_gemma":[0.0004664128,0.0002889233,0.0006821742,0.000559413,0.0004041718,0.0005451498,0.0006699561,0.0006380758,0.0004359723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002277771,"about_ca_system_score_gemma":0.0002302917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003903933,"about_ca_topic_score_gemma":0.0003941055,"domain_scores_codex":[0.9997558,0.00008474642,0.000007160378,0.00002973271,0.0001061064,0.00001644603],"domain_scores_gemma":[0.9999071,0.0000482119,0.000007569213,0.00001137868,0.00002031744,0.00000540576],"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":[0.00003143405,0.00003728816,0.000179317,0.00008106103,0.00006007954,0.0001436138,0.00005985342,0.8076943,0.01558659,0.07562829,0.001265006,0.09923325],"study_design_scores_gemma":[0.0000095976,0.00003156397,0.00005677804,0.000007698625,0.000008675127,0.00005785054,0.0000051483,0.9873037,0.001061705,0.008568644,0.002880141,0.000008509777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003086583,0.0001681787,0.9904517,0.00006883753,0.0000276764,0.00001590556,0.00000604927,0.00008855257,0.006086512],"genre_scores_gemma":[0.2489388,0.000750666,0.7327171,0.0002012843,0.00008794918,0.0003706777,0.00003499177,0.0001019172,0.0167965],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001965471,"threshold_uncertainty_score":0.006575108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01505328676321113,"score_gpt":0.2446421689699965,"score_spread":0.2295888822067854,"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."}}