{"id":"W2055239208","doi":"10.1145/2001576.2001765","title":"Rethinking multilevel selection in genetic programming","year":2011,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Genetic programming; Computer science; Selection (genetic algorithm); Consistency (knowledge bases); Genetic representation; Genetic algorithm; Class (philosophy); Operator (biology); Evolutionary algorithm; Genetic operator; Evolutionary programming; Artificial intelligence; Theoretical computer science; Machine learning; Mathematical optimization; Mathematics; Meta-optimization","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":[0.00009209054,0.00004476357,0.00003799356,0.00005955215,0.00007040575,0.00002189655,0.0002296902,0.00002832006,0.00001947243],"category_scores_gemma":[0.000005226543,0.00004231349,0.00001470514,0.0002878003,0.00001148693,0.0001928967,0.00005836243,0.00006923965,0.00002875158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002407744,"about_ca_system_score_gemma":0.00002318949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002590053,"about_ca_topic_score_gemma":0.00007624272,"domain_scores_codex":[0.9994922,0.00001394704,0.0001091058,0.0001814154,0.00007229349,0.0001309905],"domain_scores_gemma":[0.9998033,0.00001182782,0.00002308547,0.0001073149,0.00002765276,0.00002679039],"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":[0.000001323637,0.0002784676,0.02005095,0.000006428725,0.000005146598,0.000005606944,0.005785407,0.0001353188,0.0008282815,0.2977584,0.0001174881,0.6750271],"study_design_scores_gemma":[0.0001417225,0.00003418154,0.3762623,0.000008348805,0.000001072257,0.00002178645,0.00002238182,0.5663922,0.0009225789,0.05500747,0.001058487,0.0001274164],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02591192,0.00002047556,0.9711381,0.00008574095,0.00003184504,0.0001184284,6.356078e-8,0.0001462688,0.002547127],"genre_scores_gemma":[0.4616887,0.000001678124,0.5381114,0.000031471,0.00001118695,0.00002902278,1.474655e-7,0.000001630325,0.0001248171],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6748998,"threshold_uncertainty_score":0.1725494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04628301425624819,"score_gpt":0.2512123061742538,"score_spread":0.2049292919180056,"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."}}