{"id":"W1578966950","doi":"10.1007/978-1-4419-1626-6_10","title":"Using Multi-Objective Genetic Programming to Synthesize Stochastic Processes","year":2009,"lang":"en","type":"book-chapter","venue":"Genetic and evolutionary computation","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Brock University","funders":"","keywords":"Genetic programming; Computer science; Set (abstract data type); Construct (python library); Process (computing); Feature (linguistics); Process calculus; Stochastic process; Selection (genetic algorithm); Feature selection; Machine learning; Artificial intelligence; Mathematical optimization; Theoretical computer science; Programming language; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008225774,0.0004275701,0.0003343701,0.0003179151,0.0005298089,0.0001325632,0.0003880348,0.0002145054,0.000008222325],"category_scores_gemma":[0.00003479855,0.000474634,0.00007959262,0.0002519206,0.0001097706,0.0001950417,0.0002233175,0.0002068571,0.00006167081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002192686,"about_ca_system_score_gemma":0.0004559416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002149867,"about_ca_topic_score_gemma":0.000006672009,"domain_scores_codex":[0.9977615,0.00003185271,0.000476288,0.0009506798,0.0004122474,0.0003673813],"domain_scores_gemma":[0.9985925,0.0001379913,0.0002525894,0.0003332658,0.0004551884,0.0002284588],"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":[0.00001983177,0.0002248135,0.00003457418,0.000169434,0.0001398134,0.00003151893,0.0008179612,0.3580496,0.00006740869,0.0168178,0.0004183538,0.6232089],"study_design_scores_gemma":[0.0004761556,0.0003885034,0.01754154,0.0004355013,0.0001581154,0.0005158278,0.00003677682,0.873738,0.000004513431,0.1007938,0.00468569,0.00122552],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003402317,0.004075922,0.992589,0.0002550119,0.0001452141,0.001071316,0.00002215459,0.000213295,0.001287862],"genre_scores_gemma":[0.02758341,0.00009374412,0.9655688,0.0001526563,0.0003061584,0.00008189928,0.00003872496,0.00004638539,0.0061282],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6219833,"threshold_uncertainty_score":0.9997705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02846351217099083,"score_gpt":0.2645522546837153,"score_spread":0.2360887425127245,"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."}}