{"id":"W2123092006","doi":"10.1007/978-3-540-78671-9_16","title":"A Comparison of Cartesian Genetic Programming and Linear Genetic Programming","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"Verafin (Canada); Memorial University of Newfoundland","funders":"","keywords":"Genetic programming; Computer science; Directed acyclic graph; Graph; Linear programming; Implementation; Directed graph; Symbolic regression; Genetic algorithm; Theoretical computer science; Cartesian coordinate system; Benchmark (surveying); Algorithm; Artificial intelligence; Programming language; Mathematics; Machine learning","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":[0.001787379,0.0003744462,0.0006659965,0.0006505914,0.0003072227,0.001560626,0.0008313943,0.0006721254,0.005190385],"category_scores_gemma":[0.007982411,0.0001847611,0.0002684892,0.001747579,0.001015592,0.001506367,0.0009574339,0.0006422789,0.0004294296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009882296,"about_ca_system_score_gemma":0.001300155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004432431,"about_ca_topic_score_gemma":0.00427645,"domain_scores_codex":[0.9988017,0.0006149545,0.00002919419,0.0001021922,0.0003843933,0.00006767813],"domain_scores_gemma":[0.9966142,0.002599663,0.00009941591,0.0002306152,0.0003974051,0.00005872843],"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":[0.0005929642,0.0001321808,0.002313389,0.000554656,0.0000778207,0.00006145731,0.0003885066,0.2659005,0.001859187,0.32096,0.003755529,0.4034038],"study_design_scores_gemma":[0.00008331431,0.0003900492,0.001856144,0.0001196781,0.00007537345,0.0001730115,0.0002271159,0.8606319,0.001432414,0.1235351,0.01144987,0.00002608085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1287514,0.00877548,0.7724427,0.001437208,0.0003344743,0.0001034463,0.0001343747,0.001254437,0.08676645],"genre_scores_gemma":[0.5986072,0.003742554,0.3883114,0.0002011248,0.0001074468,0.00007785538,0.0001932932,0.0003121437,0.008446983],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005190385,"threshold_uncertainty_score":0.01736355,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02144646033196938,"score_gpt":0.2759214630943454,"score_spread":0.254475002762376,"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."}}