{"id":"W2783272115","doi":"10.1109/icmla.2017.00-41","title":"Evolving Adaptive Traffic Signal Controllers for a Real Scenario Using Genetic Programming with an Epigenetic Mechanism","year":2017,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Memorial University of Newfoundland","keywords":"Mechanism (biology); Computer science; Genetic programming; Traffic signal; Epigenetics; SIGNAL (programming language); Real-time computing; Artificial intelligence; Biology; Programming language; 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":[0.000362834,0.0004723495,0.0003028163,0.0003456731,0.0002202255,0.0005041403,0.0007182735,0.0006528694,0.0009827899],"category_scores_gemma":[0.001133481,0.0001762598,0.0003434075,0.0002591763,0.0005417941,0.0003184971,0.0004573996,0.0005928052,0.00009079045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003803308,"about_ca_system_score_gemma":0.0004891957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001384818,"about_ca_topic_score_gemma":0.001235878,"domain_scores_codex":[0.9998845,0.00002985956,0.000004707701,0.00002816169,0.00003263943,0.00002014166],"domain_scores_gemma":[0.999755,0.0001372475,0.00003208126,0.00002228105,0.00003661598,0.00001691015],"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":[0.00004582966,0.0001082139,0.001365928,0.00004507673,0.00005047247,0.0001485674,0.00009836035,0.9255518,0.02624006,0.01107013,0.0002113745,0.03506425],"study_design_scores_gemma":[0.00001847871,0.00007571606,0.0002666069,0.000005684426,0.00002249328,0.000030893,0.00002183165,0.9926925,0.003955986,0.002174686,0.0007257755,0.000009292158],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4321327,0.0002027994,0.5607507,0.0002110845,0.00007880606,0.0001140512,0.00004249452,0.0004443869,0.006022776],"genre_scores_gemma":[0.8533469,0.0001618779,0.1441151,0.00007591097,0.00001209109,0.0001701818,0.00005670188,0.00004047929,0.00202071],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001384818,"threshold_uncertainty_score":0.003287792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03431590936950275,"score_gpt":0.2731317682483509,"score_spread":0.2388158588788482,"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."}}