{"id":"W1531902512","doi":"","title":"Combining Competitive And Cooperative Coevolution For Training Cascade Neural Networks","year":2002,"lang":"en","type":"article","venue":"Genetic and Evolutionary Computation Conference","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Coevolution; Artificial neural network; Computer science; Cascade; Crossover; Artificial intelligence; Retraining; Evolutionary algorithm; Quality (philosophy); Machine learning; Ecology; Biology; Engineering","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":[0.002133273,0.0009631678,0.0008545724,0.0008151605,0.0003994208,0.0005700442,0.001505364,0.001467394,0.001642014],"category_scores_gemma":[0.005662818,0.0004740752,0.0004641438,0.0006502235,0.0005700728,0.00116173,0.001026302,0.0007439347,0.0003184926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005864722,"about_ca_system_score_gemma":0.0005072192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003616558,"about_ca_topic_score_gemma":0.005516419,"domain_scores_codex":[0.999557,0.0001347498,0.00003649028,0.00008454247,0.0001281636,0.00005906284],"domain_scores_gemma":[0.9987251,0.0007242582,0.00007881584,0.0001035918,0.0003094665,0.00005880413],"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":[0.00009447718,0.0001535172,0.002488071,0.00008085695,0.00008953574,0.0001446219,0.0001382852,0.8264515,0.009746823,0.004451743,0.0007091349,0.1554515],"study_design_scores_gemma":[0.000006358091,0.00003430435,0.0001141591,0.000002950978,0.000007581258,0.00001411131,0.000004750264,0.9981523,0.000846505,0.0006507208,0.0001628482,0.000003370248],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2259856,0.0006101557,0.7650945,0.0002608523,0.00009304739,0.0002374861,0.00002564953,0.0006950516,0.006997811],"genre_scores_gemma":[0.8751143,0.000151556,0.1226398,0.0001037962,0.0000277838,0.0001883215,0.00004463971,0.00004166764,0.001688147],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003616558,"threshold_uncertainty_score":0.01128191,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04332755254035137,"score_gpt":0.2507727391580069,"score_spread":0.2074451866176555,"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."}}