{"id":"W2015047009","doi":"10.1016/j.neunet.2007.04.009","title":"Multi-objective evolutionary optimization for constructing neural networks for virtual reality visual data mining: Application to geophysical prospecting","year":2007,"lang":"en","type":"article","venue":"Neural Networks","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Artificial neural network; Computer science; Genetic programming; Artificial intelligence; Set (abstract data type); Principal component analysis; Genetic algorithm; Multi-objective optimization; Pattern recognition (psychology); Data mining; 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":[0.001749395,0.001099265,0.001074237,0.001064638,0.0006384611,0.001040809,0.001288964,0.002264892,0.00151345],"category_scores_gemma":[0.004989584,0.0008265899,0.0009448793,0.001038738,0.000852914,0.001124038,0.00106083,0.001345121,0.0002405043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001101514,"about_ca_system_score_gemma":0.0009262189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007856773,"about_ca_topic_score_gemma":0.006572402,"domain_scores_codex":[0.9997129,0.0001250894,0.00001856715,0.00004896348,0.00007027165,0.00002412405],"domain_scores_gemma":[0.9985618,0.001010495,0.0001038964,0.00005375704,0.0002314229,0.00003864364],"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":[0.00001698183,0.00002050252,0.0001422309,0.00001806939,0.00001626254,0.00002042499,0.00002003339,0.9799882,0.0003809223,0.00167248,0.0001244671,0.01757937],"study_design_scores_gemma":[0.000002445215,0.000003744536,0.00001631924,0.000001482853,0.00000177919,0.000002303468,0.000001961621,0.9994459,0.00008837969,0.0004058503,0.0000287904,0.000001126341],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02420555,0.0002734569,0.9735551,0.0001676756,0.00003302179,0.00004354999,0.0000276604,0.000139903,0.001554019],"genre_scores_gemma":[0.4170703,0.0002491761,0.579519,0.0001167394,0.00003536047,0.0002946966,0.00009079645,0.0001096056,0.002514483],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007856773,"threshold_uncertainty_score":0.01562214,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04843541151773481,"score_gpt":0.3534208836653585,"score_spread":0.3049854721476237,"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."}}