{"id":"W1989361868","doi":"10.1109/cec.2010.5586139","title":"Fighting noise with noise: DE with individuals shaking to tackle noisy problems","year":2010,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Noise (video); Benchmark (surveying); Computer science; Noise measurement; Algorithm; Monte Carlo method; Test suite; Population; Artificial intelligence; Machine learning; Mathematics; Noise reduction; Statistics; Test case","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":[0.001112412,0.0008211084,0.0009421212,0.0005141378,0.0003582214,0.00066928,0.0009282319,0.000905193,0.0008691918],"category_scores_gemma":[0.002223152,0.0003209163,0.0005569931,0.0005158148,0.000581229,0.0008245572,0.001036489,0.0009705506,0.0001698528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003188915,"about_ca_system_score_gemma":0.0004169313,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007098842,"about_ca_topic_score_gemma":0.0007615127,"domain_scores_codex":[0.9995704,0.0001464169,0.00002807384,0.00007230356,0.0001468524,0.0000359637],"domain_scores_gemma":[0.9992507,0.0004615449,0.00008280895,0.00007816489,0.00009588131,0.00003092736],"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":[0.00004973114,0.00004476817,0.0006311753,0.00007693833,0.00005991015,0.00006044731,0.00005896063,0.9339383,0.007091339,0.01373915,0.0004137535,0.04383547],"study_design_scores_gemma":[0.000009049028,0.00003288858,0.00008743966,0.000004722514,0.000009733351,0.00002833397,0.000008344549,0.9943446,0.001747185,0.002684306,0.001038341,0.000005060124],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0169704,0.0002247359,0.9809799,0.00007641888,0.00003863679,0.00002722254,0.00001599406,0.000101199,0.001565524],"genre_scores_gemma":[0.488106,0.00044122,0.5069283,0.0002103643,0.00008312801,0.0002209017,0.0001223336,0.0000901541,0.003797555],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001112412,"threshold_uncertainty_score":0.005883098,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01412849840730612,"score_gpt":0.2667874087732113,"score_spread":0.2526589103659052,"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."}}