{"id":"W2130130539","doi":"10.1109/tsmcb.2008.927249","title":"Score-Based Resampling Method for Evolutionary Algorithms","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Korea Institute of Industrial Technology","keywords":"Resampling; Chromosome; Evolutionary algorithm; Computer science; Fraction (chemistry); Algorithm; Realization (probability); Function (biology); Selection (genetic algorithm); Process (computing); Score; Artificial intelligence; Mathematical optimization; Mathematics; Machine learning; Gene; Statistics; Biology; Genetics","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":[0.003416167,0.0008833463,0.001143128,0.001396449,0.000569626,0.0007879891,0.00154506,0.00107894,0.001477422],"category_scores_gemma":[0.01086941,0.0002700314,0.0008595261,0.0008922457,0.0007819604,0.0009341131,0.0008048418,0.001124531,0.0005362672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005926012,"about_ca_system_score_gemma":0.0007344976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001776419,"about_ca_topic_score_gemma":0.001780479,"domain_scores_codex":[0.9973913,0.001188292,0.0001229473,0.0002983117,0.0009236125,0.00007546409],"domain_scores_gemma":[0.9970052,0.001533282,0.0002111499,0.0003451291,0.000821941,0.0000832211],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002372819,0.0001808027,0.002026971,0.0002403011,0.0002097273,0.0001955488,0.0001991237,0.2999377,0.02020007,0.06541905,0.00345951,0.6076939],"study_design_scores_gemma":[0.00001988151,0.0000791592,0.0004297555,0.00001017822,0.0000221892,0.00009504051,0.00000955699,0.9866934,0.003605065,0.006246212,0.00276606,0.00002347803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002686061,0.0001209583,0.9965354,0.00004587834,0.00004256504,0.00004136912,0.00001045529,0.000158666,0.0003586205],"genre_scores_gemma":[0.1340804,0.0002132681,0.8632622,0.000112034,0.000135387,0.0003299406,0.0001626775,0.0001188736,0.001585238],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003416167,"threshold_uncertainty_score":0.01806664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04447657491174278,"score_gpt":0.2769372254744446,"score_spread":0.2324606505627018,"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."}}