{"id":"W4379801796","doi":"10.1002/cjs.11775","title":"Objective model selection with parallel genetic algorithms using an eradication strategy","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Group for Research in Decision Analysis; HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; HEC Montréal","keywords":"Feature selection; Selection (genetic algorithm); Computer science; Model selection; Machine learning; Genetic algorithm; Artificial intelligence; Population; Algorithm; Feature (linguistics)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.005704458,0.00145252,0.001708272,0.00181587,0.0008808979,0.001110702,0.001916271,0.001450266,0.00137232],"category_scores_gemma":[0.009285815,0.0006464389,0.000996817,0.00115807,0.001671365,0.001029848,0.001838036,0.001814793,0.000293485],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001441178,"about_ca_system_score_gemma":0.001767594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006362946,"about_ca_topic_score_gemma":0.004376276,"domain_scores_codex":[0.9979572,0.001237666,0.00006885618,0.0001769266,0.0004159243,0.0001433666],"domain_scores_gemma":[0.9941056,0.004362165,0.0003746989,0.0002939395,0.0006926383,0.0001709496],"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":[0.00008788559,0.00009770184,0.001045301,0.00001778954,0.00006361675,0.00007236831,0.00006231463,0.9553531,0.0006931476,0.008699733,0.0003543354,0.03345271],"study_design_scores_gemma":[0.00001759748,0.00001928343,0.00004677638,0.000002073223,0.000004757533,0.000006023653,0.000003174381,0.9976233,0.0001076996,0.00210616,0.00006095638,0.00000224581],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0674957,0.000175167,0.9293288,0.0003674593,0.00002867866,0.0001472038,0.00002306013,0.0005023649,0.001931615],"genre_scores_gemma":[0.6637955,0.0001128626,0.3327909,0.0003228044,0.00006816398,0.000508862,0.0001271752,0.0001589737,0.002114771],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006362946,"threshold_uncertainty_score":0.03016841,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03842926554301982,"score_gpt":0.2630375572372838,"score_spread":0.224608291694264,"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."}}