{"id":"W1529894968","doi":"10.1109/cec.2015.7257022","title":"Flow of control in linear genetic programming","year":2015,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Alternator; Computer science; Control flow; Flow (mathematics); Genetic programming; Flow control (data); Domain (mathematical analysis); Task (project management); Linear programming; String (physics); Control engineering; Artificial intelligence; Algorithm; Programming language; Mathematics; Engineering","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":[0.001790251,0.0004081277,0.0003059924,0.0006135239,0.0004517492,0.001005406,0.0006739323,0.0009025898,0.001774652],"category_scores_gemma":[0.004809768,0.0003187231,0.000446899,0.0006595493,0.001555882,0.001009119,0.0006158624,0.0007753493,0.0002168852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001566231,"about_ca_system_score_gemma":0.000970116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004613078,"about_ca_topic_score_gemma":0.00247083,"domain_scores_codex":[0.9992678,0.0003607493,0.00002733492,0.0001076256,0.0001742674,0.00006237324],"domain_scores_gemma":[0.9985445,0.001136626,0.0001111535,0.00006235716,0.000111237,0.00003424789],"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":[0.00006593588,0.00005126548,0.000776385,0.00006612675,0.00001482844,0.00006565703,0.0001502129,0.806524,0.001741146,0.1390536,0.0004190916,0.05107177],"study_design_scores_gemma":[0.00001933633,0.00003307344,0.0000920287,0.00001108052,0.000006615547,0.00001358818,0.000009681371,0.9263868,0.0006552406,0.07190422,0.0008602538,0.000007991906],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03454898,0.0002661709,0.9582381,0.0002319309,0.00002587878,0.00006776843,0.00002549764,0.0002936218,0.006302133],"genre_scores_gemma":[0.7144051,0.000471438,0.2799521,0.0001795774,0.00003638284,0.0002406069,0.00008161592,0.00008139502,0.004551841],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004613078,"threshold_uncertainty_score":0.01136386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01849450196504734,"score_gpt":0.2539878208490026,"score_spread":0.2354933188839553,"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."}}