{"id":"W2108346947","doi":"10.1109/tsmcb.2002.999814","title":"Dynamic page based crossover in linear genetic programming","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Crossover; Genetic programming; Pairwise comparison; Generalization; Computer science; Population; Linear programming; Tree (set theory); Block (permutation group theory); Constant (computer programming); Algorithm; Combinatorics; Mathematics; Artificial intelligence; Programming language; Medicine","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":[0.00110227,0.0004839868,0.0008287983,0.0006374035,0.0003615892,0.001077158,0.001016416,0.001218931,0.002154443],"category_scores_gemma":[0.00409962,0.0003797864,0.0005236519,0.001382461,0.0011564,0.001295278,0.0008543236,0.001208773,0.0005799261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009346321,"about_ca_system_score_gemma":0.0005655608,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001549735,"about_ca_topic_score_gemma":0.001084295,"domain_scores_codex":[0.9992803,0.0003333558,0.00002561578,0.0001062908,0.000196743,0.00005771653],"domain_scores_gemma":[0.9990112,0.0006992215,0.00007000402,0.00007975664,0.0001040114,0.00003581827],"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":[0.00009622097,0.00006609028,0.0004942063,0.00009668568,0.000045086,0.0001761097,0.0001453335,0.7729333,0.002679563,0.1054801,0.002035399,0.1157519],"study_design_scores_gemma":[0.00003255504,0.00007464571,0.0001531416,0.00001636596,0.00002091117,0.00007467077,0.00001087031,0.9398366,0.0011336,0.05609564,0.002532603,0.00001849862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02049441,0.0007364465,0.9719507,0.0002236895,0.0000750551,0.00004805308,0.00003216384,0.0004862924,0.005953161],"genre_scores_gemma":[0.6390866,0.001256305,0.3441461,0.0003472081,0.0001591324,0.0004196055,0.0002197734,0.0002619362,0.01410336],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002154443,"threshold_uncertainty_score":0.007207334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01716474405128253,"score_gpt":0.2353421798431268,"score_spread":0.2181774357918443,"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."}}