{"id":"W2039001632","doi":"10.1111/0824-7935.00156","title":"Applications of Rough Genetic Algorithms","year":2001,"lang":"en","type":"article","venue":"Computational Intelligence","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Saint Mary's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Rough set; Genetic algorithm; Generalization; Interval (graph theory); Algorithm; Computer science; Quality control and genetic algorithms; Artificial intelligence; Data mining; Mathematics; Machine learning; Meta-optimization; Combinatorics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009372229,0.00009260705,0.0001111645,0.00008575262,0.00008016741,0.00004752569,0.000792607,0.00003488685,0.00004353624],"category_scores_gemma":[0.00001336813,0.00008725438,0.0000542853,0.0006354509,0.00007950937,0.0001556001,0.000132766,0.00006731372,0.0001557935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001875817,"about_ca_system_score_gemma":0.00005944597,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001939054,"about_ca_topic_score_gemma":0.000001048459,"domain_scores_codex":[0.9989833,0.00002454208,0.0002986656,0.0002742181,0.0002649933,0.000154318],"domain_scores_gemma":[0.9991798,0.0001570971,0.0001008099,0.0003020053,0.0001986725,0.00006163179],"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":[0.000001512766,0.00008545087,0.0004660672,0.000006483763,0.000009079708,0.000006645146,0.000184013,0.2102209,0.00000537586,0.1756082,0.0001527747,0.6132534],"study_design_scores_gemma":[0.00003388991,0.00004675668,0.00408556,0.000006955328,0.000002571458,0.00005533155,0.00001552082,0.7069708,0.0001095418,0.2809587,0.007599175,0.0001152263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0005212501,0.0004170846,0.9940237,0.0003301353,0.0001005816,0.0001539106,0.000002919689,0.00006857064,0.004381886],"genre_scores_gemma":[0.5337599,0.00006132768,0.465851,0.0001779629,0.00005006245,0.00002560541,0.000005103941,0.000003776726,0.00006528986],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6131382,"threshold_uncertainty_score":0.355813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03296094597918826,"score_gpt":0.2959399751979899,"score_spread":0.2629790292188016,"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."}}