{"id":"W1997899762","doi":"10.1145/1276958.1277165","title":"Learning recursive programs with cooperative coevolution of genetic code mapping and genotype","year":2007,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Killam Trusts","keywords":"Genetic programming; Probabilistic logic; Computer science; Fibonacci number; Set (abstract data type); Encoding (memory); Population; Function (biology); Grammatical evolution; Theoretical computer science; Code (set theory); Mathematical optimization; Artificial intelligence; Mathematics; Discrete mathematics; Programming language; Biology; Genetics","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":[0.001237964,0.0006018448,0.0005960077,0.000493862,0.0002935603,0.000639352,0.001259734,0.0009878648,0.0009118193],"category_scores_gemma":[0.005576003,0.0004478889,0.0005238955,0.0005106206,0.001052752,0.0009834043,0.001364828,0.0009851923,0.0001489376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006149624,"about_ca_system_score_gemma":0.0006738995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001690023,"about_ca_topic_score_gemma":0.00205099,"domain_scores_codex":[0.9996061,0.0001567109,0.00001715753,0.00009068894,0.00008928771,0.00004017713],"domain_scores_gemma":[0.9984,0.001211332,0.0001009803,0.0001287651,0.0001199955,0.00003902566],"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":[0.00004808858,0.00008100401,0.001657576,0.0000431556,0.0000397565,0.0001206125,0.0003093939,0.8486882,0.006584574,0.04609745,0.0003942218,0.09593598],"study_design_scores_gemma":[0.000007569648,0.00001962399,0.00007336675,0.000002100549,0.000006049929,0.00002048598,0.00000897163,0.9907928,0.0009590054,0.007912708,0.0001934088,0.0000038843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1422441,0.0000576135,0.8548066,0.0001125428,0.000009374787,0.00005231864,0.00001117148,0.0003120877,0.002394139],"genre_scores_gemma":[0.6794823,0.00007409564,0.3178304,0.00009701448,0.00001168104,0.0003078727,0.00006433981,0.00007130358,0.002061001],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001690023,"threshold_uncertainty_score":0.006547034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0137825266600641,"score_gpt":0.236318520043595,"score_spread":0.2225359933835309,"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."}}