{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002570639,0.0006223577,0.0009327373,0.0007115643,0.000608184,0.001190029,0.001600294,0.001099323,0.001653904],"category_scores_gemma":[0.006463254,0.0003365645,0.001006478,0.0008424674,0.001378137,0.001602071,0.003066074,0.002055165,0.0003111553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007686587,"about_ca_system_score_gemma":0.001052331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002130953,"about_ca_topic_score_gemma":0.002429118,"domain_scores_codex":[0.9982576,0.0008328393,0.00006620858,0.0001931273,0.0005408362,0.0001094303],"domain_scores_gemma":[0.9974491,0.001816395,0.0001540201,0.0002634864,0.0002229058,0.00009400795],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00007611128,0.00009037523,0.001873082,0.0001913335,0.0001499151,0.0001956035,0.0006021963,0.6325585,0.01216731,0.1833136,0.0009612549,0.1678207],"study_design_scores_gemma":[0.0000256571,0.00007772291,0.0002544129,0.0000261871,0.00002668975,0.00004684036,0.00003843844,0.938736,0.001388694,0.05656114,0.002803194,0.00001497059],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01600841,0.0002245497,0.9811023,0.0003220059,0.00003412508,0.00003386239,0.00001145214,0.0001149079,0.002148365],"genre_scores_gemma":[0.4003731,0.0005205466,0.5961357,0.0002940136,0.00009463143,0.0002015698,0.00004787901,0.0001435466,0.002188993],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002570639,"threshold_uncertainty_score":0.01359504,"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."}}