{"id":"W1531850458","doi":"10.1109/cec.1999.781916","title":"Rule acquisition with a genetic algorithm","year":2003,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Crossover; Computer science; Generalization; Genetic algorithm; Genetic representation; Association rule learning; Artificial intelligence; Population-based incremental learning; Algorithm; Knowledge acquisition; Data mining; Hierarchy; Variable (mathematics); Quality control and genetic algorithms; Machine learning; Theoretical computer science; Mathematics; Meta-optimization","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":[0.001520469,0.0007175102,0.0009526967,0.001486157,0.0005475307,0.001438935,0.002004279,0.001196346,0.004961854],"category_scores_gemma":[0.004794245,0.000430742,0.001018887,0.001134539,0.0008805207,0.001031392,0.001082085,0.00141185,0.001682665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006773389,"about_ca_system_score_gemma":0.00116364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003416863,"about_ca_topic_score_gemma":0.003094863,"domain_scores_codex":[0.9988165,0.000322599,0.00008515883,0.0003357437,0.0003624374,0.00007763995],"domain_scores_gemma":[0.9983336,0.0009938175,0.00008286205,0.0002319663,0.0003144325,0.00004334394],"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":[0.0001576574,0.0002547609,0.002179473,0.0001871237,0.0001547294,0.0002933089,0.0002638241,0.2687462,0.00938133,0.03271986,0.004118442,0.6815434],"study_design_scores_gemma":[0.00004928764,0.00009868913,0.0002815048,0.00004482993,0.00006357427,0.0001779672,0.00004289872,0.9658173,0.005531966,0.01672198,0.01115055,0.00001941863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007324615,0.0001321519,0.9860635,0.0001257785,0.00003118407,0.0001639557,0.00006503753,0.002013837,0.004079967],"genre_scores_gemma":[0.06812238,0.0001727611,0.9284041,0.0001645791,0.00002678816,0.0002216715,0.0002539705,0.0001327738,0.002500904],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004961854,"threshold_uncertainty_score":0.01659906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006259371295358735,"score_gpt":0.2094776923583368,"score_spread":0.2032183210629781,"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."}}