{"id":"W2101804758","doi":"10.1145/1276958.1277058","title":"Pareto-coevolutionary genetic programming for problem decomposition in multi-class classification","year":2007,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Killam Trusts","keywords":"Genetic programming; Computer science; Population; Artificial intelligence; Classifier (UML); Pareto principle; Machine learning; Computation; Overhead (engineering); Pareto optimal; Bidding; Evolutionary computation; Class (philosophy); Mathematical optimization; Multi-objective optimization; Algorithm; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004156464,0.0001139823,0.00009552213,0.000162144,0.0002023253,0.00005613292,0.0003716188,0.00007929597,0.000002463769],"category_scores_gemma":[0.00001049074,0.0001155575,0.00005382987,0.0005324984,0.00003844397,0.0003480544,0.00006205193,0.00008421784,0.00001959961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001701867,"about_ca_system_score_gemma":0.00006569063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003675994,"about_ca_topic_score_gemma":0.0001497747,"domain_scores_codex":[0.9986822,0.00002153414,0.0003764235,0.000418002,0.0001579815,0.0003438439],"domain_scores_gemma":[0.9992772,0.0001111726,0.00009212525,0.00030183,0.0001348068,0.00008282081],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002242706,0.001638077,0.01689797,0.00006383896,0.00001908737,0.000006404912,0.0004798383,0.002188961,0.01025269,0.5749457,0.001408958,0.392076],"study_design_scores_gemma":[0.0006051796,0.00006889497,0.2686834,0.00001687228,0.00000369725,0.0000201292,0.00008822901,0.7118645,0.0003235773,0.01057372,0.007544844,0.0002069737],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008858236,0.00009816619,0.9879728,0.001225534,0.00007147594,0.0009805551,0.000002828211,0.0001970604,0.0005933358],"genre_scores_gemma":[0.3954031,0.00000600355,0.6040289,0.00006146148,0.00003402212,0.0003260222,0.00002572759,0.000006011753,0.0001086775],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7096755,"threshold_uncertainty_score":0.47123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03475190511847994,"score_gpt":0.3155368064473615,"score_spread":0.2807849013288816,"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."}}